{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# One step univariate model - ARIMA\n",
    "\n",
    "In this notebook, we demonstrate how to:\n",
    "- prepare time series data for training an ARIMA times series forecasting model\n",
    "- implement a simple ARIMA model to forecast the next HORIZON steps ahead (time *t+1* through *t+HORIZON*) in the time series\n",
    "- evaluate the model on a test dataset\n",
    "\n",
    "\n",
    "The data in this example is taken from the GEFCom2014 forecasting competition<sup>1</sup>. It consists of 3 years of hourly electricity load and temperature values between 2012 and 2014. The task is to forecast future values of electricity load. In this example, we show how to forecast one time step ahead, using historical load data only.\n",
    "\n",
    "<sup>1</sup>Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, July-September, 2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('..')\n",
    "import os\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "import math\n",
    "\n",
    "from pandas.tools.plotting import autocorrelation_plot\n",
    "from pyramid.arima import auto_arima\n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from common.utils import load_data, mape\n",
    "\n",
    "%matplotlib inline\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "np.set_printoptions(precision=2)\n",
    "warnings.filterwarnings(\"ignore\") # specify to ignore warning messages\n",
    "\n",
    "# Set for demo purposes (shorter run)\n",
    "demo = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the data from csv into a Pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 00:00:00</th>\n",
       "      <td>2,698.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 01:00:00</th>\n",
       "      <td>2,558.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 02:00:00</th>\n",
       "      <td>2,444.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 03:00:00</th>\n",
       "      <td>2,402.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 04:00:00</th>\n",
       "      <td>2,403.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        load\n",
       "2012-01-01 00:00:00 2,698.00\n",
       "2012-01-01 01:00:00 2,558.00\n",
       "2012-01-01 02:00:00 2,444.00\n",
       "2012-01-01 03:00:00 2,402.00\n",
       "2012-01-01 04:00:00 2,403.00"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy = load_data('../data')[['load']]\n",
    "energy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot all available load data (January 2012 to Dec 2014)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HNB0ihLikCn3K/PlFS1B+0t7Hww4DdtvNjSykudGtAkYph1w5JFQOCUmIypZ/9Oja5yoM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\niIiIiAjMHBIRERERERGA/wNQA9G0HxP+TAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f99bf593fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
    "plt.xlabel('timestamp', fontsize=12)\n",
    "plt.ylabel('load', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot first week of July 2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nN9wAzzwDixf7jiQ8UusNo1A5BK07FBERAXjlFTcY8vTTs3P8nBx49lk3wfT8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\nRg0/MWzc6K7CqZqUGWGeWKoKcfrilhw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POMF3JH6khpNE4f8DJYfB0J+8LNKbS3+Mcb/o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\niKDkUERERERERFByKCIiIiIiIsD/B2XBUr9Eb1QlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f99761589e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy['2014-07-01':'2014-07-07'].plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
    "plt.xlabel('timestamp', fontsize=12)\n",
    "plt.ylabel('load', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create training and testing data sets\n",
    "\n",
    "We separate our dataset into train and test sets. We train the model on the train set. After the model has finished training, we evaluate the model on the test set. We must ensure that the test set cover a later period in time from the training set, to ensure that the model does not gain from information from future time periods.\n",
    "\n",
    "We will allocate the period 1st September 2014 to 31st October to training set (2 months) and the period 1st November 2014 to 31st December 2014 to the test set (2 months). Since this is daily consumption of energy, there is a strong seasonal pattern, but the consumption is most similar to the consumption in the recent days. Therefore, using a relatively small window of time for training the data should be sufficient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_start_dt = '2014-11-01 00:00:00'\n",
    "\n",
    "if(not demo):\n",
    "    test_start_dt = '2014-12-01 00:00:00'\n",
    "else: \n",
    "    test_start_dt = '2014-12-30 00:00:00'\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Vz5h0o4lU493UMi1IIworLTGslBCygmknrLSnRz4OkOUO2rYjDkdG6BySzlKy\nSwiHwk6lukqZfQnS3nw650rccLlUxFDPUCBhpX5yd4rlIi49+VJXHM4V55CMJgHInMN2CtLoRbVk\nnMmiH5h3dabzwsauAaLD46SLY51zKHWu9KJ4IuO/r58Wh4loMM7h/fvvx9NjT4vGAB4XUBj6mowm\nYdu2qGKpvg7SjSZSjXejL4icQzqHhJAVjWWZhZUG5RwWi07LjIEBOoeks+hQJKWUkUDRIWF+cQvS\nhGXi0CpbGOgZCMw5bBWeVSwVcemWS/HwwYcBOIU7UtEUAJlzqN0uLc4l6OsnbVwNyMWhdrmmc90t\nDk2KK+VLeQzE5a6cqbum7/8gcg71hoUUPU9p0RwtfCXnpu9jOoft4W5qCcNKvd9BJuKQrSwIISuS\ndpxDE3GYSMjFYSzm5DfSOSSdxNuCwTS0VIJpzmHJLqEv1hdYn0M/zuGr1r8Kvz72a+StPGYLs0jF\nKuJQ6hxWikFIXdiclUNfrE+0EFzpzmGrQkL1MHUOdSEOiZtt6hwWy0X0x/tRKBVE7r6pONTHMxG+\niYisEivFYWdopyCNH3H4wZ98EA++9KD7s65WSueQENLVZDLAmEH7LpM+h7ogjUlY6dCQTBxaFhCJ\nAKkUnUPSGKts4d5994rG6LBSAKJwJJP+Z4B5nzerbKEv3hdItUa/zmEymsTmgc3YO7UXmWIGvbFe\nAELnsNKbLxqKinbtS+USiiVnAS91DuPhuLk47GLnUF9L6eLYLUgjvLfacQ6lAkq/b9JcRS0OpYWE\ndBir1N1023QYOIfSysekGj+bWs3GtRKHn3roU9j+ne1V45hzSAjpem6/Hbj2WtmYctn5k0oFV5BG\nKg6LRUccJpMUh6Qxz008h8tvvRz7j+/3PUaHlQIy5zBn5RAPx1GyZXmKxVLRKOfQKlvojfUG5hyG\nQ+GWzmE0HMUZI2fg18d+XR1WaugcSsRhvpRHT6RHHEKmXbKV6hxGw9GmVWbr4YZsGoaVSkKd22ll\nEQ3JhVdIOcty6bnpAjiS+8RbiZXOYfB4q5V22jnUv09vMti2DatsIRFN0DkkhHQ309PAzIxsTKnk\nCK9EwrwgjVQcDg/LCtJYlnOsVIphpaQxWqR979ff8z3GG1YqWVTkrBwS0YR4l1o3845H4nLnMNYX\nSLl7PyXhdQsKLQ5nC7Nt5xxKxGHOyjniUHj9C6WCG54oyXFcDs6haeVX03vLxIVtpyBNLBxDMpoU\nHw8wazmiYAXtAAAgAElEQVQzEJflOHrPzcg5ZEGatqgqSCNsZRENN9+gOjRzCP3xfkxmJwE4/9eE\nVMh38SeKQ0LIkjE7K28Sb1lAOOyIPJOw0ng8uLBSOoekGTpnTeJk1IaVisRhJCF2rrTwMgorjQUc\nVurDOTx58GQcmD5QHVZqUK00EoqIKjxmi1kj57BQKiAeiYtFxnJxDk3D6kxClgulAgZ7BsU5hzrn\nVtrKwjQcFZCLw2JZ3jrDdTeFc9TvVRBRASsZb5XlTjuHL8+8jPPWnId8KY9sMetu8sXCMRakIYR0\nN7OzMkcOmBdeJs5hUGGl3pxDOoekEfo/dsmi39ufT7Lj7IoTw/yWrg8rbdHnUDuH63rXYXR21Akr\njcmdQx2eGw3Lcg61cyi9jvlS3siB0scIolosAPxg9w+wb2qfaIx2YU2cw95Yr5FzOJQYEm/GREIR\nJCJy59Akn69d51AUVtpmtdIgNn5WMlXVejucc3hw5iA29W/C6uRqTMxNuGOi4SidQ0JId2MiDnVY\nqbSwDAvSkG7DpDdZqVyaDysV7Di7YaVC50rn8/VEepArGYjDABaQ2k314xyu712P0dnR6rBSgXOo\nRaVJzmE8EjcKK42FY0jFUmJxGAvHRA3f2+GKr1yBv7r7r0Rj/BQSajTOZOOhUCpgqGfIaDPGJOcw\n6LDS/ni/WVhpJGF0bwURMr6S8VYrLZaLvnPB/YjDmfwMBuIDWJNag4nMRJUQ7dqCNEqp05VSWaXU\nrZ7HLldKPaeUmlVK3aOU2lwz5tNKqaNKqQml1KdqntuilLpXKZVRSu1SSl0e1LkQQswxDSvV4jCI\nPof5PMNKyeKgw3skCzPtZACysNKsNe8cSltS6MWxUehfAM5h2S7LnMP0KDJFM+dwtjCL3livWBzq\n4xuFlYbNwkpXJVcFJg4BWYsIYL64j9Q5LNkl3yX5vZiEleoFvEkri3bCSqWOb7FUFBcuqnI3hQVp\nhnqGGFbaJt6etdFQ1HeYuq7yG1IhlO1yXVGZt5zNqNWp1RjPjLuFxaKh7nYO/w3AL/QPSqlVAL4F\n4EMAhgE8DuBrnuf/AsDvAXgFgPMBvF0pdY3n9321MmYYwHUAvqmUGlnkcyCEtEnQYaWmBWlMq5Uy\nrHSeT34SOOWUpZ5Fd6EXA1Inw6SVRVXOYUBhpUEWpAmpUEPBZtu2K3LX963HkdkjrsgDhM5h0aly\nqsf4bTmgnUtT59BYHBaDE4dSIdpOzmFPpAelsqzyrg4rFW3GeHJuTfL5pO+b3jgwcUWlrSx0XqQ0\n5023EqFz2B7e4mKRUESU96xFZSQUqbuxlS/lEQ/HsSa1BuOZcXHxp8DFoVLqSgBTAO7xPPwOADtt\n277Dtu0CgBsAXKCUOqPy/HYA/2Db9qht26MAdgC4qvL7zgBwEYAbbNvO27Z9B4CnAbwziPMhhJjT\njjiUOod6nElBmoEBR1D6bT1F53AhzzwD7JOlJK14jJxDT1ipuJVFxOm7Jg4rVeGm4vChAw8tEEml\ncilY5zAUbrhQ8i6memO9CIfCODhz0KhaqRaVemHm1z3Uu/0mrSzaEodBOocGbpdpzqHJuEKpgMH4\noGie+v6X5hxqp8Y051Di5JXKJSilxKHHeuNB2kqE4rAzeKNAJN8l3rzzRuP09/2a5JqqnMOuLEij\nlOoHcCOA9wNQnqfOBfCU/sG27TkAL1YeX/B85d/6uXMA7LVtO9PgeUJIlzI76wg1y390lrFzqNtL\nmBSk6elxxhZ8rkO8rSwoDh02bVrqGfhjZsb/+9wu+j91Uc6hZ0Hht7gAUO0SSFwab0W9RovB133x\ndXj44MPuz9qpS0VTgeUchhBCWIXrLpR0pT7Nm055Ex499ChWp1YDMHAOK+GoInGoW4IYOIe6Wqkk\nHFKLw6AK0gBmYaUmglm7eSbicCgxhDnLMOdQcC/rkNlkNCkOKw2pkJFLKXUA3XHCPns5K4ehBMNK\n28WPyGs0Tn+fNRqXt5y+qjqstNsL0nwUwOds2z5c83gvgNp6yzMA+ho8P1N5zM9YQkiXol1DaQ9B\nk4I03hYYUnEYi8mOx2qlC+nvd/4+dmxp59GKkRHg6quDOVaxVER/vN84rFQSiqQrQ5oU/9CLimaL\nlztfuNP9t1vhUeiamKKdw3Cofqindqg0//y7/4xv/uE3ccnGSwAASikoqKYhiul8GrZtVxWyES/o\nwlFxeK63CbtkXK6Uw6pEsM6hSVipSc6hV1SKxWGPYbVS4b3s5ioKi72Y9CvU+Y3S66FDnaXVMukc\ndobasNJOOofesNKJuYnqVhY+chsjLV/RIZRSFwL4bQAX1nl6FkB/zWMDANINnh+oPOZn7AJuuOEG\n99/btm3Dtm3bms6dELI4eMXh4KC/MaZhpbrKqUlYqRaH2awTYup3jgwrnUcL8gMHHAHWrVgW8Oij\nwRyrWC6Ky897w0rDyn84ZDv5XeFQuKErpwXV7snd1WMMQvFM0TmHIRVyRZ6+RsD8IlizaWATNg1U\nW9k6tDQUrr9n3v+pftz+ztuRKcz3R4yEIr5CtABPQRphCJ9uZWHbtngBPzI00tVhpVX3pMGGhVTU\n6Iqe0tzZVmHVdcfZltsCQ+oCDvTIvhN0YRlpM/ViqYhYyCCstJTDYHwQ+4/v9z2GLEQ74IDz/dNM\nHL50/CVsGdwCwKc4rBSkqc05fOiBh5D/SR43WDc0nVtg4hDApQC2ADiglFJwHL+QUuocAJ9FJYcQ\nAJRSKQCnAthZeehZABcA+GXl5wsrj+nnTlFKpTyhpRcA+M9GE/GKQ0LI0qFFoYlzaBJWqsWhiXMo\nOR6dw4VoIS8R9EvF1FQwxzF1Dr0LA1Hz9lAU5UjZKISv0bH0LvR4ZnzBHKVVEAG4rp+zTPCHVwzq\neYbhEYel6rDSeujQ0igWvu7FyRcBAMeyx6rCSqMh/70O3YI0woJAhVIBsVAMKqLEVU6HE8OBicOw\nCvsWyhpvKX/xhoUKG4WV9sX7jO5/adVRPc6klcVgz6BRYRkTBzYcCosdx7yVx/re9exz2Ca1USCN\nvkvyVh4n//PJmLl2Bn3xPrFz6BWHl192OUI/C+HD130Y4VAYN954Y91jBhlWejMcwXchHPH2WQDf\nB/A7AL4D4Fyl1DuUUnEA1wN40rZtvRV5K4D3K6U2KKU2wslZ/CIAVF7zJIDrlVJxpdTvAzgPTvVT\nQk5YDh0KbpFrSiYDrF4tbxOhw0NNw0pNq5xKxSGdw3n0tZMI86UiqNBXq2yJXQLd0w9ovdvsxS2I\nYuIcqnDDRYgWBGOzY1VjTFwTANjx8x3Y8fMdojEl23EOgfrXpNY5rEezojSPHHwEAPDy9MtVVU6l\nBWlMnENv0RCp4zXYMygWh4dmDuHxw4+LxgBwBbME0/BQbx6s9Fr2xnqNnHNpKwvXPRdukJiGlerr\nYXIdpeK8WC4GVol4JbNgo6/B9492aKdyU+64VqJS91VdnaxuZQH4K2QWmDi0bTtn2/a4/gMnHDRn\n2/akbdtH4VQX/SSASQAXA7jSM/ZmAHcBeAZOsZk7bdv+nOfXXwng1XCqoH4CwDtt2+7yzBZCFpeT\nTgKuuGKpZ9GcfN7JRZMUAOlEWKlEoOj+iJLjeVtZUBw6aHEoEeZLQX9tksIiUiwXxY2rq0KRhGGl\n2rmSLnIjoUhDIaoXGWOZheJQWsQDcBzIibkJ0RidcwjUX2RJnMN6HJg+gJP6T8L+6f3IFDJGOYem\nzqH3fZOOMxGH7/n2e3Dx5y4WjQGAZDQpHlMlTgzDSsXOYazPKL9R2spCn5uJczjQI2tJYRpm205Y\nb1+c4rBd/FYr3TO1BwAwlZ1yK9PqzbCm1Up1zmFmwt1AAOCr12GQYaVV2LZ9Y83P9wI4u8nrrwVw\nbYPnDgB4Y0cnSMgK4KWXlnoGjSmVnNYQyaRMHGqRZxpWGoksfnVUr3M4N+ecpyBKbkWyXMTh0JBT\nsTQIiiV5zqFpb6xiuYiIiiAUCsnC6mxPWGkdIVosFbE6uRoz+RnkrBx6Ij3VYaVC53CuOOcufHzP\nsexxDuvkRrbrHB6YPoA3bHkDdo7vXOAcShpXa+dwOl9bQ68x+lqGVMioz2S+lHfyVEPh1oMAKJh9\nUbUS3/XQ97LU8fKGQ/oVQ2W7DKtsOc6hwf0vzZ81dc8LpQLWpNbIhGglD9korFTJw0qLJcc5ZLXS\n9vBbkGbPpCMOJ7OTVW5js3G6WmkqloJSCtO5aXecn3YWgfc5JITImJgAfvpTs7HdnN+VzzsuXjxu\n5hzGYo5DV/K3NnbDSiMR/2OAeRdQGlYajTp/QqHgWiN0M9mscx27Paw0aOdQXJDGri5II62WaeJA\nNStIo4XXmtQaN7TUuzCWugtZKyu6HkD9nMOqOXbAOXzHWe/Aut51KJaL6In0uMcycQ5NRIaJu2MS\njhqPxH2/1otf8elFi1ZTx0sihlxxbnL/VwSUOKw0FHZaWUgL0ph8JxjkDpo6jsVykc5hB/BGgTT7\nLvGGlfoWh5WwUgBYk1qDQ+lD7negn3YWFIeEdDkf/zhw2WVmY7u5GEo+7wi8WEwmGLQ4VEoWIqod\nx3BY7hxGo45z6Fds6zkCDC3V5HJO8aFudw7DlTVuuXFXg45RLBXdRvG17RcaUVvhzm9YaVXOoUEI\nX6NFiM6JW9u71g0t1XPsifQgb+V9nxvgOIdSR2Kxcw4PTB/AGSNn4O7/fjce+bNH3GI5rdp7VM3B\n4xz6dRuBml6AkgW8YYP5WDjm+7Ve9PVv1g6kFr3RIWmvYtu2uxkgEofl+aItJgVpwiqMsl0Wf04T\nUXkri8GeQXGoub4eJudm8p3QG+tlQZo28dvncCwzhrAKYyo7VdXjsNm4vOUUpAGAtam1ODRzqNo5\nbPEdRHFISJczPGw+tpsdq0LBEXexmJlzCMjaUuhx4bDMOTTpq+idow4tPZEZHV0+4lDfi0G47lbZ\nQjwc95UD4h1j0udQLyqkIsNPtdJoaKFzGA6FoZRCLBwTOQwm4tCbc1gvD7Nd5/BQ+hA29G1AOBTG\nqze+2n1c7ByGor6bUGu8OW9GjqOwr6VeUErR11xUEMVzb/m9jlpQKqVEosYNRTUIYY2EIlBKNb1H\n6h5PGfY5FOYcaucwqH6RxVIRvbFeWGXL9+YUWYj3u7xZFMh4ZhxnrjqzLefwcPqwKOeQ4pCQLmfN\nGudvweY7AEfMBEU6DVxzjWxMu2GlgCxENOiwUjqHDr/+NbBhg5P/OjjY/WGlen5BvGe6SIBk4W8a\nVuqtaCh1CcIq3LAgjXbl1qbmnUPvAkaad2gSVurNOay3WGrHOcxZOcwV5zCSWNicU9znsNKLTtLy\noZ2w0mg4Kq5O2+o6NUKfk0nopbSwkl5QS8SQ65IZhlUDsurA3mI7EqfYpFqpdg5NczfF/RErGx09\nkR7ROFJNbUGaRhsPE3MTOHPkTHHOod7oWZNag8Ozh7uzWikhxAwt8qRFMpLy4nHGjI0B3/62bIwW\nh6ZhpUBjoXfrrcCvflX9WCfCSv2KQy0oATqHz1Y60u7Zs7ycwyDeMy0YJIssv+XPG40zWRw3K0jj\nhpWm1i7IOQQg7g83V5wTh6t5cw7DoYXuTu2Cqh6NXKGx2TGsTa2t23fRxDk0LRqiQ3Ql40ycQ9Ow\nUn0dRK50ZaOj3nvWcIwnrFqSK2cqshcs4IWft2g4KtoMaNc5lF5/0/Y2+t5iaKk5fgvSjGfGcdaq\nszCZnUShVDDLOawNK2VBGkKWN8XKZ/jQIdm4RKLzc2lEPi8Pw9M5h+06h/WE3v/4H8Bf/3X9cVLn\n0KR1Bp3DeZ59dr7Iy3IQh/m8M89AxGF5vkiGb+ewbNbnsMpJMqxWWtc5rIRsenMOa51DiZuULWbb\nyjk0bmXRwDkcnR3Fut51dcdEQ8Kcw3BUFEIMVC/EcyV5WKlUjEaU875JwwWtsoVEJCHOeTNxDvW9\nJQmHbKfdg58FfN1xoTCioajIOdTtbUQFadqoVmoaVqq/SyTjSDV+cg5t28ZEZgLnrD4HE3MTSBfS\n6Iv3uc83a2WhC2ctCCtlQRpClj9aHI6OysYFGVaayznCSRL66s05bMc5bOQCjo1V/6zDSqXOYTvV\nSgHZuJXIU08Bb3ub8+/lElY6NLQEzqHPBavXyZDkQLkFaQyrLraqVro2tRbjmXF3jJ6jtD/cXHHO\nrFppk9wdX2GlDa7lkdkjWN+3vu4YU+fQOKzUtIedYFyh7Cwa04W07zH6eL2xXtG9pe9liXPovbck\nYsi0bUNtX1HfYaUG+ZR6nLTYi6nIqxLMBmGlUleaVOOnWunx3HEko0lsHtiMsdkxzORnMBAfcJ/3\nG1Y6OjvqbpCxIA0hKwAtDqXuU5Bhpfm8U92x6H/NUxVWKnUOdUXJZkKvVhx6w0qlzmGzsNLjx4Hr\nrwcef7x6jBaw0ajsuhw7JptfN5PJAD/+MfCXf+n8vFycQxNx+L73AbOyfuPzRWIE7QYW9DkUOC4m\neUJVYaV1FvD1qpUGHVZam3No1MqigXN4ZPYI1qXqO4cicaidwy4vSKOFZDovF4epWMrIlRM7cpX7\nX+KCex1wG7aRU2kyT2lYackuiXsIVoWVGuQTB7XxQKqpLS7WSBwO9gy6m2/TuWn0x+f7LfkNK9Wv\nBViQhpAVgWn1RC2gJC6ZKdoNksyxU2Gl9YRUT89CMe0NK5XmHDYLK33+eeCjHwUuvhh48cX6c5Qc\n793vBu6/3//ru5lf/hI4+2zg1ZUCj/393e8cFgpmYaX/8i/AE0/IxhgVpDEMK60qSGNQrbRhQZqK\n8BpODGMyOwmg/YI0RtVKVeNr0o5zOJpuHFYaCUV8hwzq6y8N/asqpCJ0d0zeb30fzuRlSe5W2UIq\nmhKHLEvDSr3OudQ5rMpVFFY5BeTVgSOhiDis1CpbTnsbw1YWhVJB3G7DOKyUzmFbVEWBNPh+zVpZ\nJKIJd/NtJj+DgR65cwgAfTEnHJUFaQhZAWjXSbpY1eOCcGr0MaTi0CSsVDuAQGPhtXbtwse8YaUm\n1UpbOYBveQvwla/MH8vUOZyelhcf6lbSaWBkxBHWX/wisG5ddzuH+l7q7zcLK5VuxJgUpDENK3Vz\nDoULulbujhZeQz1DmMpOVY0BzJxDcbXSFjmH3vCtRjRzDhuFlZr0OZRWr/Q6h0EUpNGvlb4HRmGl\nZXlBmgUiT1CQRos8iWCuLUgjrVYquUcA55okIglYZUvW3iMURkiFxO6maVgpncP28eNKZ4tZJCIJ\nDMQHkLNyGMuMoT/W2jnUER2A0+cQAC5afxEAFqQhZEVgKg61GyddjP/iF/IG4KbOYSdaWdRbkOsC\nKN4NVC0qTQrSRKONxxUKwOteB1x99XyF1HbE4dxccNVNjx0Dvv/9xfv9s7NAb6/z76uuklV8bZfH\nHgN27JCN0fdkKiV7D/R9JhaHBgVpTMNK3ZxDwxCyVtVKB3sGcTx3HED1glriitq27RSkMalW2iTn\nsGyXXfHYiIY5h5kjTZ1DUc6hSUEaez6ETxpWalIdVd8bkmOV7TLKdhnJaNLo3jJtZSEpiOLdIJAI\nZu/nTVoAKhwKi9qdAPNCLxH1v6miRTZg5oqafCeY3FsrmfHMOPZO7RWN8fNdrgvLKKWwJrUGu4/t\nXhBWWu/+0puBADCSdNrwXLTOEYcsSEOWlOPHV07u1FLSrnMoDUd9zWuAu+6SjTERh50oSNMo51BX\nnffOR4+TFKSx7er+iPXGFQrOOZxzznzbBm8rC2lYaTYbnDjcsWO+WMxi4BWHgOMgBhVWetNNwN/9\nnWyMviel7Uf0OYk/o2WDgjRtVCs1cZK00AupEGzYKNvVO0faEeuL92GuOOc6HiZhpflSHtFwFCW7\nJHZbmuUc+hKHzXIOOyAOvQtqo4I0Bo6vyWZAvpQXFxFq594Kh9p0Dg1awIhEpc9WFr849Avsm9o3\nP64iRqVhpVroJaNJ3++Bvo6AMNTWbrNaKcNKXd7xtXfg1H85VTTGjyutw0oBYMvAFjw59mRVWGmj\njZVaV/L6S6/H+WvPB8CCNMuCp56SV6EMmve9D3jFK+TjhoaAj32s8/M50SgWHfEhFXmFgnmlzKkp\n2evbyTk0KUjTSnjp33f8ePU4LfL8blqUSkAo5PxpdCz9/px+OnDwoHMN2nUOpe+1KYtd0baeOAzK\nORwako/R96RUHOrXSsOB/RSkmSvOVf1H7rdxci2uS2ngElTlXNW2iagI3JAKYaBnAMdzx40L0ugQ\nqmQ0KXIPW+UctuMcjqZHsb63cbVSv0KvEwVpAgkrtfIY7Bk0Kn4jLZrjhpVKcg49DmA07F94eV0a\nk/6IQOMF/Ex+Bq/5/GvwZ3f+2YJx0rBSPU7yufE6hyZuKsNK28fEQfVTkMbbkuK8NefhoQMPVTmH\n9TZW9Aae9zvvhm03uE4iC9IsAy68EPi931vqWTTnkUeAnTvNxr7wQmfnciJSLAIDA2Zhpf39ZkIj\nLStU11bO4WIUpNGLfK/I9VYrbeTkffGL1XPxtqRo5RxGo05O3eHD1eOi0e51DuPxxf39teIwHg9O\nHA4OysfoezIocahDPRstqo/NHUPqkyns+PmOBWMAWWn9qn55wgV8s8WxN7dFh5YuEIc+HZBiuYhY\nOCYXGS1yDst2GQoLm9h7qecc2raNscwY1vbWSWKGsM+hp5WFUS86g7BS04I0JuLQZOOh3VYWJgVi\nAIOw0iYhywDw2KHHcO7qc/HooUfde8ibq2scVmroHEqFr0Sc63FsZVFNKpoSj5HkHAKOOCzZpapW\nFvXeO+/vrQdzDpcJ+/cv9QyaY7LI0nRz8Ynlgqk4LBYdcWjyHkhL8reTc7gYfQ4LBacoTa1zGIk4\nLqBt1+/JePXVwAMPyI+lheDQkHPM2nES5zCbDd45lPSnBJzr9N73tn5dPXEYVFjpQOX/T0lou1cc\nSlrH6M+mdFOlKqy0ziLryOwRAMDPXv6Z+9hscRa9MeeiNgqFrIfOQTFtZQE06CHoaROhi9LUhpVK\n8ildQSOY42LlHGatLEIq5O7c12KSc2hakEbaL89bbESUc1hynEPTPnviYkch81YWor6DHsddVJDG\nRy+6Z8afwbaTtyERSeBY9ljV8UzDSk2dQ5PejxKRDXjCSukcuqRicnFYdW+p1mGlr9/8egDzOYRA\nfefQjzikc9jlhMPA0aNLPYvmUBwuLYWCuXM4MGAmNKQ9FU1zDjvRyqKeYMvnHXHodQ51WKlSzSuW\n7to1/28/uYM6rBRwPiu14lASVmrbwTqHWhRK75Ef/hD47Gdbv65WHEpd1HbQ7+/EhP8x+p4M0jls\nFlZaLBexrncdfv7yz91QoXQ+7ZYkl4aVmhakqSqaUNtDsOwRh4khTOWqxWFPpMf3IrdKZAjbbTTL\nObRhG+UcaoekESJxWHmvxQVpvAWBfL7XukBMSIXEQjtv5d3KiNI59oQXP6x0gXNoMM5EQLnHq/Me\nPDP2DF6x5hVOu4HZ+V6f4VBYXq20TeewmUDfOb4Td70wX1DAdQ4Fucu2bTvH87TOIHA37CT4KXaU\ns3LoCTubUxesuwBH/+4o3nbGfKEAE+eQYaXLgI0bl3oGrWlHHHZ7T7PlQLthpUE4h37DSm3bCaXe\nvbszzmGjENF6zqG3BUaz0NLnnqs+lt+wUsBxDqemFopDv4JIX8egxKE+jjTHVPctbOU41opDaXGe\ndtDXUpLTHbQ41O5Co3yyQqmATf2bsCq5CrsmnF2LdCGNvrgjDqVhpSahYK16cdWGldY6h9GQ/ybg\nbTmHi5BzqB2SZmOMnEODgjQmxYeUUmKhnS/lMdAzYF6QRhhWKi1I46c3XKM5+hFQzY7XaDPgwMwB\nnDx4MtamnF50+nha1IvCSituUjKa9N1OxI+7CQC377wd//jIPy44N5P2F0op8ed0JWMSVuonfzxb\nnHcOAcc19Aq/et9bvsJKWZCmu9mwYaln0Bq9uJOExmm63Tm8+mrgj/94qWfRHC0OJe5OueyIob4+\nM+dwscJKf/ITpwjTT35SLQ6lzmHY+T++ac5hPeewVa4iALz0UuMxrcJK6zmHkrBSLTKCCis1FYfr\nK/U5WoVRLqU41PekxDkslZx7y1QcSsNKW+UBFkoFRMNRvG7z6/CzA05oqdc5lISVttvKAqi/8PQK\nqKGeITfnUC/EpUVDTEWGFn/1dtJNq5W2cg6l17+dnENTd83EORyMmxWkkR5LixrTc5P0+fQroJoe\nr4EYtcoWYuFYlXNoUq20bJddl1sSVuqnJQIAPHHkCTxx5AnYlV29Klfa731cns95ln5OVzJaHNqC\nHA2/BWl0zmE9Gn1vNXUO2cqi+xkYaP2apUYv5kzCX7tdHH7nO8Dtty/1LJpj4hzqUEdpXzn9vSZd\n5PoVh8895/SQe/DBxetzaNvzzuHkZPU4LSrrOYdaLI6NzT/mJ6y0nnOYzc7n80nCSvX1C8o51Mfz\nXic/+G2TMjvrvN+apRCHx475H6Pd5SCrlYZD4YYFWIolp0DLq9a/Ck8deQpAtXMoLcghLT9v23aV\nsGpYrdSbc5ibQs7KIR52qh1JnMMqAWuYc1hvkWXsHHoWwn7HNEILfdNqpSZtMwBZCKWeZ3+8P5Bq\npVrUSAvSNKue22qOQOPy/6bH0797TXJNlXPozefzIxq8uYOmYaXN7pMnRp9A3srjpemXqs5N5IB7\ncozpHC5ktuB/Z91XQRor2zDnGWjDOWRBmu4mVHkHpP0AQyHg5Zc7P5966IWgN0TPL90eVjoy0vo1\n9di6FfjZz1q/rhPowjKSxap2s3p6ZC6UXrhPT8vmmMs592SrY01PO331HnjAuZ/6++Vhpd7w0Hpi\nw7KcuWze7LSWqDeuXs6hFqhHjlT/Lu0KNgpFrZdzeOzY/L0lCStdKudQ+tnW5+NHHC6lc6iUbFNL\nbyCkUnJx2Ncn3wzT7kKjIjE6ZHNtai0m5hwLtMo5FIY1Sp1DnVukKo1D6x3PG1Y6lHAK0kxlpzCc\nGBdxkrQAACAASURBVAZg5tK05Rw2KO1u4hy2DCsVOIfHc8cx1DNkXJDGtKKn9FpaZQt98T5RQRpX\n1BuGler3praHZt0x5ZqwUtugkI0kjNWH46jPv8o5rIQMKqV8f069Is+0IE2jOerKu69c/0q3H6M+\nt0Y9TL3n96+P/iuA+U0mgM6hFy22juf8/2fq596qDSutxcg5ZM5h96MXqFIRZdvAM890fj710Itm\nibuj6Xbn0FQc7t8P/PjHsjGWBVxxhfxY7TqHEqGhNwKkzlU+7y/0dXoaeOUrnfv+vvuAk09uzzms\nJ9h0I/OtW4F9++qPqydSdF/IiYn5z6XfsFKvOJyacgTJqlXz47rdOZQeT1+HVuOWWhxu3CgTh+2E\nlQ4Nyb/H9YK1UUGaQqmAaCiK1anVODrnnEhVzmGLRe50bhq3PX2beyxpRcnaRUbdgjSe1hq6lcVU\nbgpDPU6jSWlYqVsFUegctmplYeIctlpkSZzDibkJrEqucp1Uv+FnJqF/7YSVWmULvbHewMJKvZVH\n/ZyfqQNYlasodQ5bVEfVcxpODLvioCrvNuzPPfeKBUkLGL2J02yOOSuHWDiGzQObcSh9qGqOWsA2\nuiYvHHsB7/uv9zk9V71hpXQOXfR3XCvn0PtdWBsO3LAgTRPnsN44VitdAehFqIlTYOLkmaAX7iYu\n4EoVh4C8omc6DfzgB3KX2EQcasEibTruN1ywlnzeWRy3Gjcz45zLG94APPusIw7bbWVRez11j8N6\n4tAbVlo7rlh0hMzAwHwoot+wUm8ri1pxGFRYqdfx9MvcnHwDAVg+4nDDBnlYadDisNnCX/f9W5Vc\nVdc5bOXKXfO9a/Ceb78Htm27oYbRUBSlcsn3Qlw7GY2OV7bL7kJEh5VOZidd59CoII1BtdJmi2Nj\n59ATMttoTCu3q2yX8dSRpzCRmcDq1GoopYzcVJOeloDM3bFtGzZsJKNJcUEa3SbFRFQC/kOk/Syo\nWx2rnb6KjcRhNBxFX6wPM4WZ+XnqvFufeYfec0tEE6KCNFWhr3XObbYwi1Q0hY19G3E4fbjuuTW6\nJi9OvggbNnZN7KoKK+32aqWPHXpMlAPYDoWycx1aieXz/uM8vP2rbwewcMOiUVhp05xDFqRZmeiF\nkomIClocmjiHJmOCRC/gTb4/pOJQv9fSvKRCwVlgS66lFiymzqFULORyjmvmxzkcGABe77TrwZYt\nZgVpWjmA8bjzuw8enBeBteGo9cJKdSN7XeHST7VSb1jpWWcBTz7ZXlipSRGhctkpEiMVXnNzwPCw\n/Hh+HealrlZ60kly59Ak5zCbde5/qTj0NgGvtzDQIZurkqtwdO6ouxCLR5x8vma7/f/0yD+5ZevH\nMmOuw6eUMnIygNaVQHUri6lcm2Gl7TqHJmGlBouskAo1Fdl5K4/wR8O48OYLkSlmXFEvbaXgbiAY\nCBqJONcOVCLivzel93imYaVA42Ivdzx3Bz7xwCdc57wdkWeSq+inoqSeU1+8D+l82s3VlYrY2mb2\nfh13r3PY6FiZYga9sV5s6NuAQzPzzmGrVgoAsGdyDwCnFcZyCiu95POX4KGXHwrkWPq9avW5GU2P\n4nu//h4AfxsPOSvX+bBSFqTpfvzm7tSjm8WhFlvl1ikES4ouGiKtzgmY9wKU5vNpR0ty/bVgCdI5\n9CMOvc7h4KDzp9MFabRzGI87wkc7an7CUWMxp5DN+Li/Y3nHAcBFFznC8vjx+RYwzcJKn34a+MAH\n5n/OZp05S51D/ful93E264hYU+ew23MON26UOYfaXZaKQ8tyzlO6yacXrI0WBrqIyUhiBJPZSfx0\n30+rFvrNBMPf/vBvkbWyuGTjJdgzuadqUWcSwucer0nIpm5lMZmdxFDCE1a6yM6htyBNR51DH60s\nmomTI7PVdr7O3ZT0OtTXJKRCTjVLHzuZ3lBfSZ89fT8moobicBHCSt99x7tx3U+vw8abNmLn+M6F\nYs2wII30mujjNQsr7Y/3I11Iuzmw7vst2IwxaWZf6xzWm+NsYRapWAob+ze6YaV+r+WeqT1YnVyN\n3cd2L7uwUr2psNjo97fZ95Zt21WOvJ8NCxakOUHR7kUQzmEuB9x2m/w4JmGl+rXdXpDGdFFtMkZf\nR1NxKLmWhYKzEJc6h4WCv8IytViWU1zGj3PY3w+cfz7wy186j3W6z6F2DoGFQq9ZC4xi0XH5RkbM\nw0pDIeA3fmP+30DzsNIHHwRuumneTda9KaWfG/37pVVm5+bMxWEk0lxAlcvO88nk/GNBi8P162WV\nWE3DSi3LKWIjdg7L833G6omMYqmIWCiGaDiK3lgvfve23616vlWft8u3Xo7Thk/Dnqk9VYs6kxA+\nPa6pc1hpZbEgrFTaykIqMjwFaQLNOWxRkOZw+jAu2XhJVdNqwN/irHYObl6Yz/fN+177dg49BZKk\nYaVhFW5YWKnh8Wp6FtY7t3g4jl9d8ytsP387frL3J1XCVyLyagvZmBSkafR505+tvpjjHNbeN34/\nA17n0MTxBZo4h4V553B01gmN8SuYD6cP46L1F2FibqK6WmmXO4eArEBMO+j3t9n9fzx3HKloys0x\n91OQxlvwqx4sSLNCMXEO9aJWWn7+V78C3vMeeQilXmxL3B3dpqDbcw7bCes1dQ6lor5YdBaeUndN\nVyuVOofSnor6eH7CIXVYKQCceqrz92I5h4AjDnVritpqpY2cQ684lIaVAsCVV1Y/3yysVLtqP//5\n/PFMHCh9/aQbFu2ElbaqoKvzGcPz2iJwcTg0JBN5XnEo+XyXSs4Y05zDRgsz78JgXe86AMA92+9x\nn2/ktpTKjlj60Z/8CGeNnIWd4zuRt/Jue4lGi9xdE7swk5/B7Ttvh23bVYtVfbxm4nBD3wYcyx7D\nM2PPVBWkMQorlTqHTcLjFi3nsIVYO5w+jA19G3DK4ClVj0vaWdSGUfq5ll6XWCrOtciTOIf6PmnU\nr7Pp8ULNnUOrbOH0kdNx6vCpOJw+jOn8NPrj/c4Yae6gaq8gTSN3x+sczuRnFoRj+w4r9YwzdQ4b\n3SOzhVn0xnqxOrkaE5mJ+XNrkasIAMeyx3D2qrMxMTdRHVa6DJzDwMRhuYiQCjX93jowfQCbBja5\nVZ395M/qXPFGmDqHFIddjl5UmIT+SfsO9jnpDlVNvv1QKDhjpeKwt9cRokEtBk1YCnEYhHOoRY1J\nzqEfB7De8fyIQx1W6sXEOWzmAHqdwzVr6oeINmplEYs5eaj6s9Wq+I13nGb79upeic3CSmv7DJo6\nUKbOYS7nr5BQLZbVehOhNqQUWB7iUDvuc3P+N9Isy1wcenuh1aLDSgHg1CFnN+UNW97gPt9oXLqQ\nRm+sFyEVwsUbLsbjo49XuXmNFrkfvOeD+KNv/hH++Ft/jMPpw8gWs0hG563fVvl8qVgK/+uS/4WS\nXcKq5Cp3jNQli4Vj4vDExcg59LpUdcf4cA439G7A3//232PX/9zlPm6Sc+jOURgOLAnr1U6eNOdQ\nh0NKRX1VWGkDoafPZWOfEw45lZ2vhNtKdN361K34l0f/xf09XgHV8YI0oaiTc1hIL3Dc/b4H3nGS\nKr+1zmG9e0QXpNH5y7Xn1mzjYTI7iTNHzsR4ZnxBBILfjZ+lIkjnsDfW2/R7a3R2FOt717tVnTPF\nDFIxpxFwq5DlRpg4hwwrXQaYOAVapJkWRJG2wDApiKKdQ6lzFTR6UW0S/iq9/vr6BeUcRiJmzqF+\nryX5ovo+9htW6mUxCtJot087h7btnI8O9ex0WKlXHCrliFJNs7DSWnGo3+ugxGG7TnEz4VVPHDbq\nFbkY+K2g60VvPEQizvvm930olQzDSu35sNJG4WraORzocXZVWhWIAZwF0WCPk/T6qg2vwmOHHnPa\nS1TyABs5lXkrj/968b8AOHlGekHZ7Hi1wuvGN96I7IeybrsNo7DSpcg5rCO8vC5JozHNqpWOzo5i\nfd96JKNJnL36bPdx0yqbknBgb0EaaX5dT6RH1OdQO4cmYaV+nMNIKOLkys0cWnAfNxPL9+y7B999\n4bvusaQiu94cmy3gdVip91hA88+AulHhidEnFhzLdAOhVUGaocQQZvIzKJaKC8IaG12Tyewkzlx1\nJiYy1WGlFIfzFMsVcdjke2u2MIu+eJ+Tm52bwrG5YxhJOJXrjMWhgXPIgjTLAL+Lai96IWgqDr3l\n/f2gxaE053A5iMPl4hwmKsWq/C6stagxcQ51MRfJNSmVWjuHljVfjdNLp8NKy+V5Z1E7hzpcsFIf\noKNhpV4xWo9mbpkWV1NT88eTftaA9sShqVPcKqx0qZ1D7YqahJUCsrxDLQ5NC9K0qlYKAJv7Ny94\nvtEidzo3jYG4IybXpNYgXUhXtZxo5JwUSgVc9/rr8Io1r8CLky+6oWgav/l83gIKxmGlQecc1rkm\nrcJKQyrkK6x0wbGEYY0mYaVVOYd+HShPzqGRc9gkrPSbu77pbjzUHk/Ps/ZauqHNKowNfRuwe3I3\njs0dq3IOm13/Z8efxWOHHkPZLi90YH06h3PFOdc9b7iJU3GYk9Ek8qU8clauZQsYL4+PPg6g2kkV\n5Rz6LUgTTSGkQhhODGMyO+kr59C27SrncCY/44b1SiroBo0u3DSVmwrkeNo5bPa5yRQySEVTGOoZ\nwtjsGPKlvPv92uizvWjOIVtZdDelUvDOoUk+k6lzmEisXHFoUtETMHMOIxGZiGon5zAWk4tKP2Gl\nuoJnqOZbRzuHfkP4WuUOeh1C7Rx6BSXQvJXFqlXV4rCVc1ibc1hLK+dwcLA6rDSRcOYm6YcZtDj0\n03uzkTiU9vk0RVfQlYSHesWh5DOgw0ol9zFQU5CmjlgolAquOLl+2/V44a9fqHq+0UJwOj/tOo0A\nFvTJana83zn1d/AH5/wB9kzucd2GZsdrJbxMwkqNnMM2cw7rtaXoREGauuKwjbDGxQwr1WGNiaj/\nBuyAJ+ewSVjpdfdeh7fc9paq+bdqpeAtxrN5YDMOpw/j8098ft45bCKWy3YZzx19DpFQBAemD/jK\n76pHOp9uKYa88+yL9eF47ni1c9jiPdA5gF7HUZRzaDfv8wmgaqNH901dkE9Z59wyxQzCKoz1feuR\nKWZwZPaIUZuaoNHnErhz2GRTK1N0xOFgzyD2Tu3FcGLYrWjbaMNiUZxDFqTpfoJ0Dk0ro5rmHC4H\n51BfS5M5She5+vpJ+xxqoSfJzdOixqRaqUmuoh9xODnpODm1hEKycMNWeYBecbhmzbw49BZGqXc8\nLfJWrZrPU/SGlTaaY21YaS2txOHGjdXOYTTqfHYk7uFSOYfSnEP9vgTR4iafdwRbNOr/u8u78dDs\nfas3Lhp17hG/Y4DWBWmKpfmw0p5ID84YOaPq+UYiwxtWCqAqbxBovIDUTuXmgc14eeZlt/x9s3Gt\nhFcg1UoXMeewnVYWo7OjnXUOm1SUvOO5O3DTwzctGCMJK9XipB3nsNH7ds7qcwAAz048W3W8ZmGl\n3vNIRpP4xh9+AwBc57DZddx/fD+GE8M4bfg0HJk9ssCB9XP9i6UiCqWCu7nSNOewcp/0xfswmZ0U\nVSv15gC61UoluaI+nMNMYT6/bXVqNY7OHfXlSk9mJzGSHEFIhbBlYAseO/zYshCHel6S+7gdiqUi\n+mJ9TTe19Hsw1DOEPVN73JBSoHXObSOYc7hCMXEOi0VHdEl7obXrHK7UsFJpQSDvWAn6+olDzypi\nSJKbp0WNiXNoIg71fdxszNRUfXEIyM7NT1ip1zkcH1/oHDYrSLN1K7B3r+MA6evR6Fh6nGlYqRaH\nXudQu8TS1iVA62qllgUcOlQ9LsicQyC40FL9fkrCQ72bCFJxaPK++SlI06yMeSMnyRtWCiwUh62O\np/sq6vL3zca1FIcmYaVd0uewE87h+t71Cx6XhOP5yTm0bRvv/f578YEffQBPjz1d5ThLwkq1OJEW\npNHXv5moLJaLGEmM4IGXHnAfa1WQpvb6680R7Rw2Eye7JnbhnNXnYF3vOozNjlW3pGgi6m97+jb0\n/30/vv7s15EupNEX76tyd1qF/m3s24h9U/sWFKSpN07nq45lxhZcj1bO4Z7JPXhmzCkgsaAgTQMH\nUH8PrEmtwZHZI776HHoLWV20/iLcs+8e3+JwMjvpzjFo9D0vyZ1t63gC5/Ck/pPwxJEn3OsItM65\nbQSrla5QTJzDQsEJl1oOYaXLQRz29QXjHLYjDsNh2cKznWqli+kcDg/Xf87EFQX8icOxMeea93j6\nyNYbp899eNjJTTx2zHHidAGdxQgrnZurFodeUb8YzuH11wMnnVQ9zjSs1MQ5BIITh173XJI76BWH\nEjc7HJZ/37UqSOOtVlqPRovVdCGNvth8cq83xBRo7BJocahzkmoL0tQNK0WHw0pV9/Q5bKeVRc7K\nIZ1PYyQ5suA5v7mDgL+cw6fHnkZfrA9/+9q/xd27767KC5M6UNo5lBakCalQ07BSq2zhz1/55/j0\nQ592X9OqEqu3MiYAnDZ8GgC4Gx/NwnOfHX8W564+F2tTazGWGVtYrbSBqP8/P/4/uO4N1+Gj93+0\n6joCrVtZAMBZq87CsxPPLsg5rPce6Gus+w4u6HPYRNR//MGP4/zPnu8UwPHhHFply91oOn34dLxw\n9AVfrvR0br51yCvXvRLPH33etzi88b4bcf5nz2/4/GKyFM5hq4I02jk8feR0PHLwkarvhiCdQxak\nWQaYVCstFufFoSS/pR1xKA0r1QvmoMTh3Bzw05/Kx5lcf33NpQtcff2CcA7bqVa6WOKwmXMozaf0\nm3O4ejUwMeHkO3pbaDRzDpUCTjsNePFFZ86Dleg8U+ewmcjIZoENG+qHlZq0t2klDu+5p/rndsJK\nW1XnzGSWVhxqNy+Z9H9+XnHYrAVJo3FGzmGTgjTeaqX1aLSg8IajAsCdV96JX/z5L+bHNdilricO\nvc6hST6fJKxUi4Fm4Yn1WLQ+hz5aWTSqVjqadiqV1juuaVhpI3Gya2IXXrXhVdjYtxHjmXEczx13\nNwQk1187UNoBtH0uMPyElRZLRVy+9XIko0nsndo7f7wm7SV0iwhNMprE+WvPx/o+x41tloP5wrEX\ncNaqs7Cud93CsNIm+V1H545i+wXbcTh9GOl89SZLPTGkj6/f5zNHzsQvDv2iyrlv9B7MFZ1dq+nc\ntPu7vM5hM1E/W3DCRPZM7VngHLZyN89edTaeO/pclWBuNC5rzbezuWj9RQDgisNWmxxre9cCcERR\n0Oh5SXJn20HiHGoHfCmdQxak6XJMC9Ikk84CWCLY2sk5lIaVapERlDj8/OeByy6TjysW5c6hzpeS\nXHvAuX79/fLr4Q1Zk4aVmlYrXYyw0sVyDpvlHMZizvu7b1+1OGxWkAYATj8d2L17voiOHlNP1HgF\nRT1a9TnsRFip39zZF1+snjfgiLzFEIfd4hxKq46a5Byavm9+CtKYhJXWLg62Dm3Fqze+umqcH+ew\nXkEaqStnXK1U2C9Pz6HeYsnUOawVJ3XHNBAnuqdZ3XE+C9KU7XJ1g/MG7s6B6QPY3L8Zq1OrMZ4Z\nx3R+GoNx54vL5PqHQ2GR4+ttZdEsrDQajuKUoVPmxWG5upCKHwfkqb98ynWymuVgjmfGsa53Hdb2\nrnXCSr2tLBo4hxOZCQwnhrEmtQYz+Rkcyx6rcg6bFc3RnLnqTPzswM+wOrXafazR580Vh/npBb+r\nlbszkZnAYM8gdh/b7ese8f7uc1afg53jO5GzclWVWOvdk9li1s25vGidIw6rqsU22eTQAuTpsacb\nvmax0Pdu0M5h02qllb6G2gH/w3P+0H0uUOeQBWm6H9OCNNGobNGjjwUEE1ZqWi0TAK68UlbUAWju\n3DTDJKy0WHTEQD4vc27bye/SzqFJWKl048HUOUylnGvTKNx2MZzDVmGlgFOUZvfuhc5ho4I0wLxz\n6Ecc1h6vFr8FaWy7fXHY6hrq7wvLMt8I0ONbbRh1gzgMh2Vhpe3kHEqdQ+04hVSoaYGYVuKkkePY\nckHRJOdwODGMqdwU0vl0dZ9Dg3w+42qlUufQ44DUE7AKqunvqOsctriOzVpZNKpUCvh3DjMFJ0/M\nFb4N3reXZ17GpoFNWJNag4m5CSfn1NMX06SwiSS01HUOm4h63R/vlEFHHLptKkKNc+V8LYwbXP+j\nc0exKrkK63vX42D6oK/czSOzR7Cud53b7mH/8f0LwkrriUNv6PeWgS0olotYnawWh/WON1ecw1DP\n0LxzWNPnsJm7MzE3gd846Tfw4uSLC3MOW2wYnTZ8Gp6deBbDieGWIcveVh5re9dife9632GlWvTq\nvzf94yZMZicbvr6TWGULCir4nMNWYaXRFJLRJPa/bz+uOP0K9zm/G321GDuHLEjT3XhLoPtFh7+l\nUrK8w6DDSk3CGgHga18Djh6VjUkmW7+mHiZhpZblLAIlYWeAs2gcGDAPKzURUD09weUc6jk2Or9W\nzqHk3Lyhf63E4RlnAA8+OJ87qMc1CisFZM6ht69iPZqFlc7Nzec4ZrPtFRICmguTdNo5jm7Vod/r\neFy+GVMsdr9z2G5Y6WIXpKkN6aobHmoYVlq7YK2llVMZDUeRiCQwOjvadisLabXSaDgqdw49OYfG\nYaWNnMNmOZ9NxElTcejTOfSb83Zg+gA2D2zG6uS8c6jDGk2qxQJAIuq/KI0WNXrRWS/UttY51IK9\n2fvWKqy3mTiZmJvA6uRqnLvmXOwc31ndNqPBpooWh4BT0XPv1N6F17/FQvykfiepu1YcNhJe6/vW\nu+Kpts9hM3dnPDOO12x8DfYd3+c751DPc6BnAH2xvv+fvXcPsiwr60R/+5zMkyffj3p2V1VXV1VX\nNzSPBkTABpFBkQFHkVHxhQYTE+LM6KDXcHxMj1dGHR0IjYAxiBi94yDjSHsRVFQEJhS5OiAgNM30\nk26qurqq612ZWfk85+Rr3z9WrrPX3mc9vvWttXeebHJFVGRVnly519l7n13fb/0eX/e9dt+b5t5q\nbbRyrXDe/6b346W3vtR6LDkk6F3siIj2ZxafwcPXHjb+fMyxsbXhZPJijm5aqUtWup0Ye3zqeO41\nCuOrG5Qgp+LY8xzugrG56Q8OZVHnCw7lsXwllFtbAix4eWkCmEPAvwk4Fxyur/PAIccXxgGHaZoV\nrD7MoSorrcpzKNlNU0HtSivlyEpdnkMAeN3rgA9/2M0cqrJSlTmUa+Yyhy5Z6fCwOMb8fP7e4qSV\n2p4jly8Dt9ySJbjK9+sDzOWQm1r9Cg6l9LtWCwuk8ZGV+gbSFBuA22SephHiU3Edb2Z4BhcWL+Ra\nWZSRVrrYWcS7P/NubGxthDGHlrTGNE3L8RzaZKVL+jYW3TUSmMMiODRdtwuLF3Bs4hgOjB7A9ZXr\nuVYmPrJSlYFqDjTJfi0JapIkMQJ7eW1vn7od5xbO5Vgy+d508jhnKxHDeZTM4emZ07iyfAXz7Xmn\nrFQFh/tH9uPM/Bmn57D4WZNy0vGhbJ6NlZMsXHuj3cscGtidza1N3GzfxJ377uxpZm8Dh2qC6rHJ\nYzlwaLq3WustDA9m4PD1d7y++28KczjWGOuCQwC4unzV+PMxx/rmOsaHxr09h1eWr+CfffCf+R+P\n4DksBnypgy0rJcqx1bGXVlrhmJ8HHmIk9sqm15ywl9FRf1kpJ/ym0fBjreSxOMyVLOp8ZW4yidK3\nfxpHVqq+N98C3ldWKoFHkvCYQ8laUZNVQ1pZuMChqc8h4PfeVlezzQAKc/gt3yK+ugJpVFnp6dPA\nV78qGOwYslJbIM3wsGAP5+bCZKUDAzRwePCgAIfyWnPBYT/LSlV2mes59FknR1aaYzIs6aEc5oor\nx1PB4fMPPh//ePEfcXD0YDavBFnpez73Hvz8X/88zs6fjeY5jMoc2mS9NuZwWd/GwrRG3VhaW3Iy\nVwC6LUcOjBwQstIOT1aq3jc+vQ5VUGkC9lJWemTiCC4uXsyxZACPATGdj85GB6vrq5hqTqFeq+Pu\nA3fjK1e+4gykmW3NYv/wfgCCufvgQx/MeQd1160oPZb3mVp8mzYDVtdXMTwwjMmhSSy0F3IbRjZ2\nZ7Y1i8mhSewf2Y+b7ZvegTQAcGziWC9zaJC+FlvhqHNc4PDYxDEsdha77//i0kXjz8ccG1sbGG+M\newUrAcDD1x7Gx578mPfxfNJKdYO90cf43OwF0lQ4fvIngRcyEns5zKHc8R8Z8ZeV+gbLSKmdLzjk\nBtLIgt23TYcEha4+b8XBlZVypH+cQBqVJeN4DpPEjz0MZQ5tDcfn5+ME0qgpmK5AGkDISgGxmSKH\nK5Bm/37grruAL3whA4eSbSz+P0NhDl3gcHo6HBy6PtsSHBZlpRxw2O+yUhXk+dzLoX0Ofc4lpceY\nLKhNo57o0zIpRXWxqEvTNCdjfde3vQvvePk78PpTr8/N8w6kITYAz4HDEjyHLOZwWwppnGM4/0A8\nWSmFgZLrHB4cxsHRg/jipS+yZKWqPNGn12FR1qi9l7fXeGT8CC4uXczd/wCPATGdj9nWLPYN7+v2\nJzwyfgTPLD7jbGWhpvz+k9v/Cd7yvLfgvm++r/u60XOo+YyqUkybrHRkcASTzUksdBZyG0a2An55\nbRkTQxOYHp7GfHu+J/VVd6zi+T42cQyHRxXm0DCvKCtVh2uTY6G9gGOTx7DUWep+np+++bTx52OO\nja0NDA0MoZ7UyZsjQNYmhZomLMfa5pqTOVxdX+0L5pASSGOevTe8hq9nB8gkm5yeZjJIwpc5HB31\nDyhpNPwLSC6AkkUjt4ej2peOMmRRPTvrN4dTwHNkpSo45Ia2SPZ2VP9M0s5rNPzBoZS+cmWl1Pe2\nvJy9FwpzKFnlOcUHr5OVSkAtxzveAfzIjwD7tlsR1WriT9FjGAIO5edrZkacnxDP4eio/RxeuyYk\npevr4lzI54gN0JuGfJbY5vULc+jzuQmVlXp5Zwk9xlTQoxu1pGYscn0LCtnTTxbUzzv4PLz7Emys\n3wAAIABJREFUde/O/QzLc+jos7fYWUSj3uiCw+ZAM9xzmMZhDimtLExFZIxAGqrnUN1EePXxV+OD\nD30wJyvlMofUMA/1/BpDkrbXeHjsMK6vXEd7o52XlWqKY2ewkuE8zq7O5nrISUZVghwTc6ge775X\n39fzOjUZ8rGfeAy3T92eW6cVHErmsNDn0FTAS4Z/ujmN+dZ8joU1MYDFdf7gC34QzYGs+a/Rc7je\nwvSw/j9u07HkWOgs4IWHXojFzmL38yx7OpY95LWUDLhNnq+OFGL3d7Y1m1NN2MZWuoUUKUYGR6zP\nrfZGO3fO1VE5c7gXSFPN4KRlqlIkDnPoGyTBbZshwSFH5sYFhxwGEAAWF+0/p5tXFXMoZaVVMIfy\n/AN+zKFkQEJkpaZ72RZI4/MZUEEHxXMoh7rhQGEc3/pW4JlngMPZ5qpWjhoCDiV7pzKHHM8hhTlc\nXBQbJ1LCKo8tz4WPJLvfW1kUW1KUDQ5VXzAZUBICaVyghrvbrDuey98oj8eRlbo8h/ccuiecObSk\nNXKZQ5es1JZWavMccgNpbOm0kuF86wveiu+667tw94G7AQiQwfUccmSlNnAyWB/EYH0Q+0f249zN\ncxiqD+Xem+/5t3l11SJ8/4iQisrrYQL1sZIhn7P/Ob3AyyLZlMwhtc+h/KxONacyWalHIA0AvPbE\na3HvsXtz782VVloclEAaKSuV95KLsYo15L0zPDjslVgq38+1lWvkOfK6uT4zVnAY8Cx3+WCLQ0qW\nbXLbPeYw0uCCQ98USoAvB5NA6KqHH5grK1UDaXwZKMCfOZQFu6sJuO543EAajqz0llt4jBxgvwZ/\n+ZfAy18umr6ra5TzfGV1w8PAwoLfOimy0tBAGtkqY2i7pqCCtQcfFM3m5TCByoHCE/HIkfy/JbBp\nKDX05iYfHMrzNjkpwFuIrNTF5C0tCWBYrwNXrmSbTEmSgZqhIfN8OeT5doVU7TRzqG6q+IC84W0F\nFafPoZeslBBI4wSHFlmpyytXPB4FHLICaRyyxsXOIp5/8PndUJV+8hxymavWegsr6yu5JteUeXI8\neOVBfPbCZ7GxtYGJhttzqBaDbzj9Brzh9Btyc3zSSruyUp+00i23rFE9l7dN3oYvX/5yjpHiMCDU\nglr6Bo9MiAe67Tw2G/rivXs8TwkfYAZeEixI5jBJEpLnUAWHi53FnByVCg6Lw3RObLJSFziUaayP\n3ni0ey/5SDxDhmT9fTY55DzAExxug3PXplZns4OhAf1/siHhYr73pK19UvdnjK/sDa/RoDHWucEp\nKIAwqWcIc8hpZeGblhlDVuozKEW17licAp7DHKoMiA1A/eqvAn/+571rBOyBKKbjcTyHNuak1RJe\nvWH9/zHWQnxzE3jb24AvfSnzG24r38jg8J57MuDsM684KDJWyhw55EbP2JgAUyH3FpU5lCylPDbg\n30qEwm6awKEOmMceRe9grDYppiGZQ69jEQJpXKDGJCslFYKFeSTmkAG8pKzRtEu92FnEiakTudTF\nYM+hJ4AFLMwhIxDo8vJl3DJ2S1eiS50nx5v+6E34ib/6CVxYuOBswg7YvanyfJjO/+ef+Tw+9MiH\nAOQ3LLzSSgl99tQ1vvTWl+ITZz7RbaYu5/kyIKZjFef1MIfMtgG6e8slPZbzTMcbrA3mPYeEPoed\njQ4a9QbqtTpGG6OYb81n4DygJYLRczjIA4edzQ4OjBzYMeZwoDaA4YFhr8RSDnNIDdIqgznkbKoA\nbmnpHjiMNDjMIcenAoQxhyGewypaWeyErNQlj9PN4frCZDIqNTyryICYrvelS8CXv5w/lgoOfb1T\nsWWlsl+goVbSgjU5PvEJ4AMfAD75ybzf0DTPxeQBNFmpaZ4OHNr6HPqAwxDP4diY/XmwuCjuP9Xf\nyAWHFADbL7JSX+awqkCaYvy8rsgtS1aqKyCpzKEvOKwlNSRIjMEtS2tLODF9ArOrs/y00pL6HHJb\nWdgkpYC9lUWapri2cg1vfs6b8Zv/8Jv5QBrD8WzBOZKNMhWCb/nwW/D9H/5+AHmQR4m7lyPHHBKk\nr/ceuxcfe+JjeeZQ54MlnH8KEDowcqDr7QPCZKXaQBrLBgLgBrFqWimlz6H6WZ1uTuPK8pXuv62t\nLCz+ZVuiKkdWmqYp1jbXsH9kPxbaCzsjK60P+jOH258TGZRFGfK62Ta10jRFZ6OTk1KrQ3f/b6Vb\nJPUIh80erA9an7F7stJII0RWyvHzcQBbqOewnwNpuCmnHHCoFvC+85pNdPveUdjmYk8/HYDa2hIy\nQRUcqtLHsplDGaxUq5mZk07HLlm0AYa/+RvRfuHSpV7AYWIAbWBNzvNlAE3rjMEcjo8DFy+Gy0op\nzOHkZD6QBvBn1yhrXF3VM8VVB9JU4TmkBDL1HCstT1bqlENqQCVZVsrw80n2UFecLnYWcfvU7eHM\n4Q54Dus1/fmfbc0aJaWAPeXx2so1jDXG8L43vg/7R/bjnz/3n3dfM0n/XKm28v7SXd/zC+e7f1eL\nSq+UU6rncHuN9xy6B53NTo45NDEgMVq5HBw9iCPjR7pMrjGQhhJAxGBpXFLbkcERLHQWcGD0AKnP\nofpZPTB6AA9eeRBHxhXJrAbkqaywdo22PoemtFLD+5JrHKgNYP/Ifsy15ioHh/Lc+noO5T3vM6cr\nK7Vsasnnnwmg6+4tec1MCgTTvI2tDSMIlWOoPrTHHFYxZHHoU/RwmUNu82opK/VhhNTwmypaWYTK\nSn0KajnPt5WIKqvzBdq+PsyizE0HDm/cEPfCV76Sva6CSlsTdtPxfMChLKhV71pxqCyVbpiA79Wr\nwIc+BLz97cC5c/k2FqZ5VJDHmccBlbb+iGkq5qqy0pBAGpfnUBdIA/g9S+Q8+UzQseBpmm0sFYfp\nPo45qIx7cRSDbDiBNF6yUoVt4QTSVC4rZbJyNqAhZaWzrQDm0OU5BL/PoY1tMV03ExDrzrPISp9e\neBq3Td6GW8Zvwe9+5+/izn135uYV31uapj1tCorDBfQkM6QygD4ppyTPoQK8jk8dB4BuoirAY0Cs\nLJnSQ/Elt7wEH/uhrHddlW0D5DptIT2TTQ1z6PAcSu/ayemT2NjawLHJY91jFdN6Keu0eg4ZstK1\nzTUM1YdwcPQgrq1cQ2ezg+GB4coDaXyZQ/l+fOZIEGfb1LKxhgBPVg3w70mXMmAPHEYasiDwATWh\ngTSckBhZrFGLsxBZaZVppfL9+BwrTcW8KmWlvoC5yBzqQMalS8Add4g2BU8+mR2LW+T6ModFf6Pu\nniy2iSgOE2D42MeAV7wCeMtbgKeeojOHZYA87vFM4FDeD0nS6znksNIu76yUlR48KHoeFsGhb/hK\nrSbOh26e3AjRnZd+lpWG9jn0YiljBNJYilxXf76qAmnkPN3729za7IZWtDfaWF1fxUBtAAO1AWyl\nW+ReY8W0RrbnsLhzn+YbtRcHF5zbAmnuf+h+oyRVB+olELKxCyagJ3+XvA6xmEMdgFVlpWONsdzx\nAWYgDTHEI0kSnN53mjTPxRT7hhYB7pCYyaHMc6ie/42tDa1XVP2snpw6iUa90fVVcgNpOMyhTR4t\nw1emh6exvLbcTd6l3lOhg+s5lJ8TX5+iK620s9kx+g0BPsgzMY62TS1gDxyyxuxsvicaZchCzgfU\nVB1Io8pYqUVPSCBNvzOHXAlfiPRPNpj3bS0BmIvqK1dEu4UXvziTlnIDabjMoYulUcGqbpiAb7st\ngmROnhTgcGHB7TksC+QBPFmpPFbx/3gVnI2NCWZP7aHJ8RxSZKUHDwrJ5/x8mOcQMH8GbDLi3SQr\n9f3ceDOHgYE03IQ7HYAqy3MImEHs8toyRgdHUUtqmBmewdWVq10ZlY+0VN2VD/IcauSoVuYwhIHS\nzFvsLOI9n38PvuP0dxjnFd+bzW8oh6mdRWujhZHBERybPIYLCxdyIG+wRmcO1fOrAwzSE1q8Botr\nWUiAqfemK3UxRdoj7XUxqcbrlpbDHLq8kd1WFsr5t3lFc+Bw+iSOThy1em4p6zQBPdtzwcYcys9k\nLalh/8j+brhSZbLSwLTS2LLS9kbbmFQKBEiWNfeka1ML2AOHrHH6NPC61/nN4YLDqgNpfHe3Q1pZ\ncNg1DgMoj+c7jwvOQ0E9lzk0sWutlgBzRXAoC39fWWkIc8iVlZremzxno6OCGX3ooXiy0qqYwyTR\nzyuCwxhppRRZaZIAx48LljlEVgr0LzikModpCvz+7wNnz2bzVBl32X0OQwNpZHFcZBfKCqThtokw\nvT8JTgBg3/A+PHb9MRwYEbHCPtJS9feYQB4HwJYFzk2BKE/OPol7Dt2Df/XSf6WdpwP1LrZLztMx\nNpIVOjh6ENdXr+eZq7oHc+gIpDF5IkcHs50+o+fQ8t6SJIkuR+V4Dinn35Tg2hNIo2xGUADbCw+9\nEC86/CLnHG5aqQ1oW8Gh0rbh0NghnF84j/Gh8coDabh9Dn2YQ6qsdI853OVjft5/jiyQfFopcANp\nQkNiuMwhN7SFwxz6egc5oFJlaTjvzec8ynkcz6FLVip/74tfDDzwQH6NQDWyUhdL45KVmt6bCjLu\nvht417uAO+/Mz6uSOYwJRtVrOz4e5jmU3tlOx5yEK2WlgACHX/taWCAN0L/gkCJ1BkT/y3/xL4A/\n+zPxb67nkNXnMEIgTZIk2iRQUiCNxnPISV2ksnK696ce85bxW3B5+TKOThwFADJzuL4p2mTI38OW\nlTJAZUjjat28J2afyHkMdcfTAS9KMqGOgZJ+spnhGcy15nrSMsmeQ0cgjU7m/PRPP433/NP3dP89\nkDA9VyXKUYvDKOt1FOI2MKq2sihu0JjmdTY7aNTEz7386Mvxkbd8hHQsG5tk80Wa3p/8bOhCmVQ2\n/+DoQTy98HSlzKG8ls26f1ppPal7+xTrSb0LuHRS4PZG2+o53GMO+3zIa3rXXX7zZIHE6Q3H9Rxy\nwKFvL64QIBoiK+UEy9Rq1clKOeef6zl0BdLI9UjmME13VlZqYg5tslIbcyhBxunT4rz9/M9nr+9E\nIA23dUbxGtiYQ87nptEwg+w0Fb+vub1xefvtfOZwt4BD+bmxbeLcvCm+XrnSO48TSON6tq6sreB3\nvvg74liaQJpiUcFlvDjgRE38tB2LzRwa0iFlMXx6RnjCZLBGo94gMYfFwAy2rNTgOWSlxTJbMDwx\n+0T3PJjm6aSXFFmpjTmU4DAKc6gDsJrNitsmb+t6D+U8btiLL6g0gXrOdXOlgMrjOT2H7QUsry3n\nzokJ6HGknlw2Vd0w8JmnMoeHxw7j3M1zGG+MkzccQsf61joGku20Uk//4PjQuB9zuC0rrSU1YwuS\nPc/hLh+zs+LrxIT954pDFkhVyBPVVhYcz6FP0RNDVuoLmAEec+jbw1EFeRxQyb1uPp7DoqzUxBwO\nDAC33CJ+/4ULfFmpGpLEWaPNc8hlDiW79Vu/Jf49Pp6fFyuQhgLyuK0zqOCQy2ZLcGKaJ8+JfH+3\n3go8/XRYIA1gB4emVi1Vy0ptgE3aAC5fzuaF9jm0zXn0+qO471P3iTlKUZkkCWpJrQdoUBmv4jxn\nsIZBHmoLNQHCPIeuIvfQ6CEA6DZ9H6oPkViGYmBGCLup9RxaCmN2vzzDvOur13F47LBxnu78u9pY\nyHkm5nBkcAT7hvcJ5lAJ9gliDjX9Cl1r5PSZ7M7zLKqD5MDMQCYtGN0GzZI5XFlfyUltY4JDpw/T\nsGFB8W9qwaHCHN4+eTsev/F45cwhq8/h5jrGG+N+nkPlWW5SPOx5Dnf5kEDGF5x0On6MHBDmXQsF\nNb7gUL43W/P2a9eyoi80kIbDHPr2cFQL8Sqvmw9gVotcl6wUyNjDInPom9bImSOPxZWV6phDlYGS\n7TJc80KYQ0p/xFiyUl0gDZfNdrVSUO8HQGwkXLwY7jk0zVMZ3+LYCebQ9N6WloD9+0XiL1B+IM2N\n1RuYbc1iob3Q8x8/18+nS8zkyOPSNGUVuVzgBWzLSrdBw2RzMvcaVVZabNLNPY9sz6Gjp5/3PEfK\nLKeAHKwP6j2f65msdHZ1Nve7onoOCewmp88kYJaxsgJpKOc/oqy6yByurK1gtEEDhyag4TqWaVjl\nqBYWiuKLPDl9EgudBYw3qvUcyrRSX1npxNAES1YKwAhGK/UcpnvMYfQhwYVvIIpkNDjN7CnAqziv\n6kCaWs3NQB06BPyH/5A/Fgcc1us8cMhtScH1U/puBoS2sjBJL4vg8IEHej2H1CKXwy6XLSs1MVBA\nXOZwJzyH8rqNjIhztLrK8xyqzKEJHKrA9/Dh7LgAX1Zqum79ICulBNIsLwsLgQoOOf1BqX0OZ1tC\nmvLUzad6PEAxWzBwwie4RW5IK4v1rUxW+uPf8OP4xA9/ovsaNZCmKCuNCWDV/om6wW5lYWAOXUyZ\nlpWjAC8DYFhdX83JSkv1HDKYQ2px7C0r3QHm0Og53Ga3ttItzLXnSmMOufekSzZLkZWenD4JQPiK\ndyKt1DeQhisrBczPrUo9hw4pMLAHDr2HLMg4zOHEBI9JqtdFcUktmEKknr6JempxTjneF74gvoYw\nh74gDwiTldqaeZvm9ZOsVP2Z5z4XeOIJvqxUZQ59WJMyZaUmkCHnVR0sE5M5lOctSURz+mvXeJsq\nKjjRfXaKzKEEh9JbzQWHnOvWb7LS06czcBjiOaRsGM2uCnB4dv5sj1yLLdnUyEop3qlY8riQVhZq\nkTvaGMXr73h99zVX8SJHUVYa03NYViCNkTlM3X32uLJSW4PzmeEZzLXncvdkVM8hRR7K9ByaGBdu\n+IovqAxJ65XHS5IEk81JXFq6RGYOTeAw+j1JOJcuWempmVMAgLe96G3GwJbYo8scDnoyhyXJSiv1\nHO4xh/FHKHPIAQuAH9DjMoecRs0qOLQdT37WpXcnhDnkgEM5jyMrlc28fcAQ5/yrslJOIA1FVir7\nAXJlpRzmkCIrDW1lYRo7EUjDYRxdslIA2LdPeJ5DPYe686+CJSADh897nvjq80xQ193PzCElkGZ5\nGTh6VDC26+t8zyF14+3G6g0kSHB2/iyW15bzckhNcVymrDQWAxhSHNsSUgfr+t58xaG2sTAdq7Q+\nh5FbWXBAPYU5NBXwElhLz6EqIYzZ55DKbnIbzMdgzqnzuJsjJuZcHm9yaBIXFy8GB9LEviddQIPC\nHN46fivOvOMMbpu8zQhCY49uWulA04sFlLJS3xAbCaCtzKHFc8gJVgL4oHIPHHoOWbRzmENfcEgF\nXqZ5IbJSDnNok1/KIJ+rV8XXInPow8pxwaGrCbhujsp4UeeGMIchnkOKrPTkSdGzjSsr5XoOXRK+\nInNVHDvBHO60rFQHDuXPhngOKczhIZH9gbvvFl+5gTQmoLfT4JDKHMrej1NTIrk0JJCGKiu9a/9d\nODt/FhcXL+LI+JHuayFhL5TWAT1zmL0AY8lRgXxaaXGYQlTk+PLlL2Ntc01II9W0UmbYDpc51KWV\nhvi7fK8bV3oJ5JnD2dXZHAPixRwqslKfPofqMLWJ4Lw3lxTSBKBcYJS78WBjKlXP7aWlS8GyUhtz\n6Nzo0Mxjy0oV5hDIpKVURUDokCy4r+dQykp95uRkpSbm0OU55MpKTWmle7LSuKPdpic1LixkoIfj\nOZRMEsALhOC2svApBKns5vnzwAteIIqttbWsgKzXxR+fwlP2a/MZXFmpK3XRNi+kz2FZstJDh4CV\nFeDcOSFTlPPKZA4phfiziTksw3MIZOBweJgHDms1uuew0QC+9CXgwIHs388mWamP53BsTHxW5ub4\nfQ6pqowbqzfwjbd+owCHSxdxZCIDh+wgFUNaKaeVBYcBDCmOOd4pQBQ/L/ndl+DPHv8zfVpp4ViU\nsB1T6h83kIYj4XP2pzQkelKAl405VFtZsDyHiqxUd/6pgTS6+5/SQ9A7rTRAehnTc6seb3JoEktr\nSyRZaWej480cOgGzJXnXmthr2PhRmUN1DNb1rR5iD7lub+aQKSslMYc2zyFXVmpKK92TlcYdnQ4w\nOUkrzKamgD/902xeiKzUV9YVyhzGlpVeuiTkWQcOANev5wtfXxaqKlmpukZfWS83rTTEc2hK9FSB\nV5IAd9wBPP44cOKE+B5HHieBhO54gNgU+drXete4mzyHsZjDkD6HOjb11lv95dgyadXEHBZlpQDw\nkpdkf/cFh7tJVuryHI6NAdPTAhyq81ybKrOzwD/+Y/54tvP4wOUH8MeP/jHecMcb8Mkzn8QHH/pg\nD3PIAWwcWWnMIje0lYUJ2AzWzLLSf3jmHwAAy2vLPbLS2J5DTisLJ8iL2AKDKivVppVu5MFhj+eQ\nEUjDCYgB+D5Y3b0cEkjj8nzGWmNxnTKtt0zmkLNhwZaVFphDOSpjDrfBsK/ncGNrQ4BDT1mpy3O4\nvmXfxOEyh7rzv8ccljDabQH6XIXZ1vZG7dKS+BoSSAOYC385Ll7sneebskntxVVcI0VWOjcnmI9D\nh4S0NETWyA2k8ZWVqmvkyEqrSitVi1WX5xAAjh8XXw8eFF+54TI2UPmlL4lk1PV1mr+LIiutOq00\n5rzQPodA5tdNkvJbWRSHz/2vfm44oN7H38sdxQRdGzgcH88zhyqotD2T3/hG4GUvy47nUmV89sJn\n8WMv+TG8+blvBgBcXr6MoxNHu6/HlpWWIY8LYTdNIMoqKzXIGp+afwoAcGHhQrcdgzovpufQmVYa\nkzl0sICcnpaAgzkcHO56rFbXVzNwWPOUlarMoWe/SCBM6hyzlUVpgTSG660yhwBonsOtNSMLFdtz\nyFEhAGYA26g3yPdVyJAyWt+00q7n0Ic5VO5/UysLp2ScEawE6M9/DObQCkmTJPkDAE63WJqmP+r6\nmd0yJDiUHjrTeOYZ8VVNNw3xHJqK4zQF/viPge//fuCRR4RHSC0qy2YO220aczg7KwosCQ6Lckhf\n5tDnfcl5k5PVyUp9Zb1pmgeH16/7r9FUVBfliWPb/7dIiw1HVgpk4LCpkcn/zd+IwvqLX4wnK62S\nOdQdb6c9h4cOiWcPUE4rC1crEc4Gwm5gDm2AbWkpLytV1+1a5xe+kH02KD7ks/Nncee+O9EcaGL9\nl9Yx+KuDuG3ytu7r7OJYl1ZKYK60vjzYfXlBctSIslK5M39h8QL2j+zPp5VW3eewwkJcB0apjeJN\nrSxGBkeQJAmmm9N4euFp3DFzBwBP5nDL7jmk3sftNP8fOFfqvNtaWQDAvmHhK+i3tFIXC2UNpNlJ\n5pDpOVzfXMdYY6ybquryKgP5c2QCv86wKUPqaL8yh18DcGb7zwKA7wZQB/DM9tw3Abjp+B27arTb\nNFnp44+LrzduiK8hfQ4BMzi8dEkAQyALewltZeEjM5yfF5IrwH48HXOoykrLZg6rlJVyAmnkua/V\n7LLS//JfgHe8I/s3NZBGLfzf9CYhLZUjJHnUNO/znxfyx898hsY2UmSlJuawn1pZlOk5/KM/Ap5+\nWvy92YwfSGNjN033lm7ECKShHos7qIE0q6vi8yjB4cKCeP7LdbqeWxIcyvNvO9aZ+TPdUIaB2gA2\n/+9NnJg+0X2dyxyaZKW+TdhTEHx5AZJNo6yUkVa6trmG2yZvw4XFC11ppBzsdhuMPodBzdQNnkPf\nQBqyrFRzPLWAnxmewVPzT3UZrCDmkNvuJBKbzWW72Myho5SmpKO+8fQbAdBlpaZrrltjmqY56a9p\njcZ5XFmpznNY62/P4cbWBhr1Bhr1BhlUqteR4i/VDXmfqxt9rueP6XilM4dpmv5H+fckST4J4DvS\nNP175XuvAvBL1hXsskGVlUqZp/TXpakIUllYoB+LIit96qns7xcuiK+y0K7VeK0sfGSlEvQB7rTS\nu+8W6zl7Now5bDZFwa1KvFxjJ2SlnBAhwC4rfd/7RJ/C//yfxf1EKcSLwOv7vz/bUADEa9T3Rg3k\nWF4WAUQyiMh1H1OYKxMD1U+BNMVrIAOpXJuLFM/hSGabIodiyeHbyqI4fKSe6v1mk5WartvAgH+r\nIN9BDaSRAH1mRjzDFheFPUCu03RO5OdpZUXcN6rn08YcnpjKwGDxP30TOHSxeVXKSmPOA7bTSmv+\naaVrm2s4OX0SFxcvYnltOSfFi+45dCQ8ctNKWcyVhl0LCaRRQezM8Awev/E4JobEB2AnmMNYrDS3\nlQWH3YnR5xAAXnP7a/ADz/+BnrYsxutmuOYmBqqW1KwsmOn8J0is788k/7bJSqvyHNZrdQwPDnvL\nSqVXUab5Oo9V2BzhgEMgA/a1eq3n9xrnGNJ6KcyhLjhHDh/P4SsAfK7wvc8D+CbqL0iS5A+SJLmc\nJMnNJEkeT5LkX25//3iSJFtJkiwmSbK0/fW+wtx3JUlyI0mS60mS/OfCa8eTJPlUkiQrSZI8miTJ\nt3q8r9ygBtLcvCkYtRs3sl1yXyZPlZWaiuOzZ7O/q+Cw0eB53lyy0sVF0WbhD/9Q/IyUiwJuWem+\nfcC992ZsEpc5lI3pfdtSjIzwZaW+QC8UHOpaWWxsiE2Hl78c+Mu/7F0jVVZaHL7gnMIcdjqCnXzm\nmcy3JY/lCs3RDdN7K5M5LB6PEixTPB7lWABNVqoOCWioLWB8W1no1sdlDn0Z334KpJHX4JZbhEqD\nCg4XFoD9+wXrOD8v7oEksR9rvjWP/SP7jWsOCeTgpDVWGkhjkDXa5HG2QJq1zTUcnTiK66vXcWHh\nQs67yfWFceaFBMv4Fv2m43FDK4A8GJoZnsF8ez4Dhx7MoQqiufcI2yvKCMCxBtJYWNjY978q/67X\n6rj/e+7PATgO0Ahqps5IvTSx56YAlqrAoXzPPqm7QMbEDw/QQaUKxoLAYeFcUkCe7nNTdVrplwH8\nepIkwwCw/fU/AXjQ43f8BoATaZpOAfguAL+WJMmLt19LAUymaTqepulEmqb/SU5KkuTHt3/+BQBe\nCOA7kyR5u/J77wfwJQAzAP4DgA8nSbLPY13dIWWlLpCxsACcPi2YQy44LMpKdYXI2bNNwE7hAAAg\nAElEQVTAL/2SYJQkOJSMoyvERnc86ZUzFf1veYsAWW99K/DQQxnoA+yATTKMr3ylkBy223zmkNvn\nbXjYfpzPflYwcp/5THYsaiCQbo2+4FBeaxNzuLoqvr797cBHPpIdSw2kochKiyOkLYUNHJ461QsO\nTfcxt5VFvzGHVYHDJPFj80I9hz7H2g2tLKiBNPIaHDsmJL2rq5ln17bOmzeFwuTgQREk5PqMAjQ2\nict4VdXKIqafD7DL46yew40Obh27FXOtOZxbOJfzbgbJGg2Mi3FO7FYWLubK4C/ltIkorvPQqGh+\nKlMzfdNK5XmKCfIosjpjkIeN8e2TQBrXZgALHBr63nHSYsnzDKoA3X3ZqDe8wBp3SBmtTZ6uG/Ka\n+KScqt5ADtsrR/Fccjfsqk4rfRuAVwJYSJLkKoQH8VUAyGE0aZo+mqZdTUQCAQhPKf82redHAfxW\nmqaX0zS9DOA3t9eDJEnuBPBiAO9M07STpumfAPg/AL6H/tay0W6LQndjI0sk1Y2bN0VxrDKHvumh\nFFnpxYuiRcTRo1kIjgQaXHBo291utYD77gO+5VuEd3BuLt8vz3Q8yTBOToqvZ85kO/A+zKEs6jjM\noQsc3ncf8Iu/CHzgA+LfKqjyDeTwDaRRr7UJdEm25UUvAh57LDuWr6y0OHyZWwpz2G5n4FCGesg1\ncmSl/dLKwiVjrgocAvwm7FV4Dl2tLGwpszvBHJqOp4LDRx8V97G8ni5wOD0tWvc89VQmCbY9kzl+\nMrLn0DOkJKSVRcxADm5a6drmGkYboxhvjOMrV76C41PHu6+xZaWGwt9WZOn8ngAv2ETOs90jpmvN\naTBfXOct47cAAIs5zMlKQ86/ZysRgCkr7aNAGt8NI9e8IOZQcx5JKbN9yBxKYGt7juiGfHb6eBXV\nzYgQ5rB4L1MYQK1XsUrmME3Tc2ma3gvgDggW7440Te9N0/Qc9XcAQJIk70uSZAXAYwAuAfgreQgA\n55IkOZ8kyX8vMH/PA/AV5d9f2f4eANwN4GyapiuG171Gu01rQr2wIPrIzc/nwWEIc2hiTppNUYTI\n8BsJNHyLLBUcmorOr30N+LEfE+/tZ39WgEMZSGNjF9Qgh5MnBfNx7Jj4dxXMoQSH1gbUN4Bf/mXg\nypX8seQafZlDn+JdBQKuhMdTpwRjnKZ5BiSElSuDObz9dnFO5+fLYw77vZUFRYoKmANpYjK+Ns9b\nbM+ha8PCJnWuChxSlAvy544dE7JSuaHlWuf8vGAO9+8XjKNrcwRwF/G6ooIUEsNoghyzyA1pZREi\nK23UG9g/sh+zrdle5jBSSwSSrDQSAwXw+iOSWlkkhoI1zebeMibAYTeQxrfP4U4F0ugYR0fKY9XM\nIdfjGIs59PG7qYOSlukr0R2sVxRIkwrAZnuO6EawrDTRe6WpAF1dK4UBBHrvkx3pc5im6XkAXwDw\nTJIktSRxfCp65/8EgDEI1vFPAHQA3ADwjQCOA/gGAOMA/lCZNgbBVMqxuP093Wvy9XGfdcnRbgsw\n5gInN2+K4vjmTT44pLSykGzS/v1Z+wNZaMeWla6sCDB47Bhw+HDGXlEkU6q08ORJ4UeTRbNvWiZX\nVjo0lIVD6Mb588A3f7P4qh4L8GcOQzyHLpZsclIA3atXxTUZHs7WyGHlykgr7XREOuzMjGBOXMUx\n13NYZiuLWLJSSmhSVcxhSCsLjufQ5oM1nZeqZaW248lrIDe2VI8nRVY6MyOeJ/L+t4FsF3MYS1aa\npqmTcYkp/QttZWECNjZZ6dqm6PM22xI9p9QQD8lApcrFLM1zGALyGDJDHbvmOpY8ngtkSOZwfEj8\nR+5iDtXzu+OBNJFaWXCuW6xAGtO8HWcOuZ5DA/DdEebQQ8Yqz5UMpCEdSwHRJhkrB6C7ArG684qg\nksocbkUAh0mS3JokyZ8mSTILYAPAuvLHa6RifBbAMQD/Ok3TlTRNH0jTdCtN0+sAfhLAtydJIjN9\nlwEoe7qY3P6e7jX5+pLvuoCMqXM1Kl9YEM3GFxbEz3EDaVQ2yRTI0WgI5lCCQ/k930h4l6z00iUR\nzFCrZf3W1GErllZWBFgABDi8807avOIIYQ5tSawLC+Jc3XNP1i6g6Dn0ZU58ZaWu8CHVX3fqlJDm\nzs6KjQE5rwxZ6aOPZkUxlTmU9/zBg2KdLuaQIis1bY74tsCgBsvoZKX94jkE4stKY3oOXSy47Xhl\ngsP//t8Fm62C03rdvGkkr4HMgJAp1K512sAh13PILY6LUsMUKRIkzmTCnQ4NAbbTSm2yUkNR19ns\noFFvYK41lwujAYAkEemKKmBmew4dnrdaUkOKNAeUgABwQknL1LTb4AbSqOucaor/9OX1cDGHtV+p\n4fce+D2xhiJzWBFzC+jv5TJAlzwW13PoSouNtU62PNTQyoUi6zVJxk2yUh+ZJ3fI98zxHA7U/Poj\nxpKV9gTSODzPchQ/czGYQ/tK8+N3AKwC+FYA/x+AVwN4JzJZKGcMIPMcFkeKDLw+AuAeAF/c/veL\ntr8nXzuZJMmoIi29B8D/NB30ne98Z/fvr3nNa/Ca17ym+28f5nD/fuE1uXGD5zmkyEolqJAhOZ1O\nVtCsrfFkpSYAtb6esTQ3t7tXqv/nmQrIrS0R5CB9N295C/DqV2evV8EcqlJPXVLihQuCEd23T7y+\ntNQrj/P1XLnAYbsNvPSlwK/9mmCZXeFDKkt2662CObxxA3jxi7N5HFbOJqtbXQWe9zwR1vNN3+TH\nHDaboq/lY4+5i2OurHRryx9UUti8kFYW/QwObcxhLM+h+rkx3cs2GWtZ4HBzE/iZnxH3pXr8JMmO\nWZQoq+/l7rvFRgllnQsLQoI6NQV87nPuEJs0TVnhExzGi9obq8pAGhNzYpWVOvocNuoN/PWP/DVu\nn7rduE5ZsAV5Di078CoQVYuxkKAR3/6IoYEosmA9Mn4k95qNOXzkmii/rq6I5svRPIeRQGWQ59OV\nFhvpc0NaJ2OezgdLYaC0wSYBslKTUqIy5jDleQ7luW0ONKtPK9U8y1myUgtz+OlPfxqf/vSn8ej1\nR/HQtYfMv9N51GzcC+C2NE1XkiRJ0zT9ynYris8C+H9ck5MkOQDgtQD+EkALwOsA/ACAH0yS5GUA\nbgJ4EiJx9L0A/jZNU8n+/Q8AP5Mkycchgmt+BsB7ACBN0yeTJHkQwC8nSfJLAL4DwPMBfMS0FhUc\nFsfqKo05vHlTALapKeFhK0tWKtmkJMmkpRJUdjp85tDUSkEWq//234r+deowFZCykbQsPu+8M4w5\nbDR4slIbc3jzptjlT5KMhS1bVvrkk8AjjwC/8zvAL/yCu7m2Cmrltb5xw80culpZ2EDGX21v7Zw9\nK8Chj+dQMod//dfltbJwga/YgTS+80LAoYvN83meqMzhyor+9TLSSvuJOXz4YQHavvhF8VxWjy/Z\n8yI4VD87v/iLwIc+RFvn2pr4P2LfPrHxdM894vvGPp/bu9hWNi/Ac1VkyTi9sSg9FWOCSoCfVirB\n4bee/FbSOstmrjbTTdSRB4cccEKRNRbTSmMFoty1/y4s/+Jy9zUbcyhB4dXlbXBYYA6LjEuZ5z+W\nrFRu4Pg2fA8B567rzZWVcu6RMmSlOqA9WKvIc7i9dttzRDckE8+VlcZmDimyUh/PoSTE/uKrf4Hf\nfeB38fiHH9f+nI9fcBNCTgoAN7fB3gqAI+YpuZEC+NcALgCYA/BuAD+VpulfAjgJ4BMQXsH/A6AN\n4Ie6E9P0dwD8BYCHIMJm/jxNUxWQ/gCEZ3Eeor3G96RpOuvx3rpjZUXsAruaUMt+WNPTguHhgkNX\nWqkKICcmgAcfzApBW0H3yCPAD/1QvlB0BdKoBd2tt4r56jAdT5WU6kZVzGG9bpbMquBk3z7hreTK\nSqng8IknRN/Hz35WHE8Fh7ZAGkAAwhs38q1EbLJSl+fQ9N6efFJ8ffzx7L1RmcOhIcEcAm7PFdfz\n5gJftZpgt1WGu8pAmt3EHJbhObRtWFQNDj/6UcGC/6//JT478p6UxzSpJeQ1eOtbgT//c9o65bN7\nZkYk9jo9t4R2A9zCsygr5bI0aeoOvwliNw3FMTetdGjAbEYugl/uOaHIuji9B60hHhbmSpdWGpO5\nGm1k/5HbmEMJ/i4uXeyuIdRzGHJv+cpKbUDU12MaIqsOYZhNoIHTkgXgy0p9k1+r9hz69jmUG3k+\ngTSqbNd4zVKm5zAycyhHTFnp5wG8EcCfAvgkgP8XggH8om2SHGma3gDwGsNrfwTgjxzzfwHALxhe\nOw/gn1DW4RoS6DSbdnAi5afT03zmUJWVUtIa771XSDaHhjKplKmg+7u/A+6/X8gZf/3Xs+PZ5JAu\nkGE63vJyvhDTzSvbc+hiDtVzPTMjQBdXVirn2VqCAAIcvvKV4vz81V9lPk6KrPTAAeGNVJnDMmSl\nc3OidcZXvyr+rRb1JnCyuSn+DA4K5hDYOeYwScTrKjP2bOhzCPS351BuBnE2A8oCh7//+8CHPyxS\nlt//fuBd78peM22Q2K6BCxzKtj0AYXOEGGMeRVZKKARDwm8qk5XWBo3FmfQcUo8Xwly5ijMdYGMz\nh4QWJOw+e56BKDbmsLPRwYGRA3hm8RkAeSkz23MYsYcgB5xz2zbsRCsLm8+U05IF4MlzTfOAnZeV\nyuspz2GaplblhhySBWwONOl9DrfyzHk05pBgEQB6N0iqTiv9EQivIQD8NIC/BfAwFIZvt44vfQn4\n8pfF3yU4dDGHspBXZaUuQPMbvwG89rVZIV5kDm2BNADw3vcKOegtt9jnACIk4Yd/GPifivNSFkI2\nKZgLZOiO5wKH/cAcqud63z4BDkNlpa602CeeAE6fBt78ZlG4SnDowxzGkpWa3tvsrGBbrgp1EElW\nKteZJMDLXia+5wILIZ7DMgDbszWQpizPodwQkOvuR1npjRsiJfmFLxTPJFdbijS1b4hRUk514FC7\nOULoRcf1k2llpSUWuTGZk5C0Uhs4LBZLpXreNDK+IObQ1QuzInBiawHQ2ezg1MypLnOoMhVGybKj\nMNe+N/DuLRfQM24EuCSUFYLz7jzPlhtB8mjPljimeYD5mdeoN7yYPO6Qa0+SxKhc0A3Jyg4P+MlK\nVeZQx7izPIcp3XPoK0eN2efwZpqmc9t/b6Vp+qtpmv78dlP6XT3+238TfwBRVLiYw62tjDmhyko3\nN4F3v1v0D/zt3xbf82llAQh2ZnISuO02+xxAsE7f+q1ibbLAVFtgcBMlTbLS2Myhb7iPK600tqzU\nBrLleOIJ4b185SvFHJfnUE0rPXAA+MIXRAEri1wb8HIxvqb3Njcn2OWlpey9uWSlKoh97WuBT35S\nFOTyWJx2G1zmUM71BWyxmMN+7HOo+9yEeA5XVkTY1M/9XO+6+4k5VDfsgF5wWDyX8nqbrp+LOWw0\nRDIzkIVx9YOslBxIEyl1MTQQxeY5NBWRLnBYXGepnkOOrJTrOSwhLdN0PBs4b2+0cWLqBK4uX8XG\n1gY6Gx0M1Ye684rvjSJZDgHnvue/HxJVgZL6HEYM9qEm4fYrc+jyAepGznPoISuN4TnUegcjew7l\niAYOkyQZTJLkPyZJ8lSSJO0kSc5u/9vSonp3jDNnMs8VhTlUQ2KostKHHxb+rJ/7OeGNAfzSSuU4\ndixrLi8j2gsp2gCAc+cEa3X4sPDEqMezSf+4stKd9hzK4pfiOfSVlX70o0KiNjubX6MLUEpwKMG8\nS1aqbgQcOCD8gO9/fxazz5Vs+oBDCnMoJdVyfPu3u8ECBZwU35u8r11KkJ1mDvupz6HpGUQB56b7\n/8EHxVruv1/8TGgrC5+NGOpI00wdMD0tvqeCQx17HvK5kXPlsW7cEF+NGzgU5rAAMmR7BCfjwuiN\nFdKLLtY8wC1rtDGHEozoBgscGjxXHD8lB5zIQJQyAmkofQ51azR5DjsbHYw1xjAzPIOry1extLbU\n7Y/IvkdC2GyNx843BZS6ESN/NnSNgJsp5gANXXsV8n3MlNr6+CmrCqRRAdJg3d6zUx2q55AjKzUx\n7hxZ6U56Dn1kpe8G8G0AfhyiVcS/gkgffZdt0m4YZ85kUk+K51BlTqjg8PHHRfrnHXeIfoJbW7RA\nGpVNAoCjRzOwofqtiuPyZREqc/vtAigCNOawDFlpFZ5DyXiZmEMVZPvKSr/7u0Xa6Be+kF+jDEPR\n9U+bnxcg6vDhDMzLYpUiK33xi0WQzfd+b/Y6V1bqAofHj4uAJfm7JNgxeRVtjem58lDdGqmSzVjM\nIYUFLM7bTbJSLrv82GPiPhwcFM+S4qaK6Z40geYymEN5LtUerS7mMIRxL27aPfWU+Gp6JriKQIAH\naAC9rLSsVhZB8zxldbbd/s6G3XNYBJZBnkNKWmPhvVG8g7q2Aa5AFG4gDUeeaAPnnc0OmgNNHJ04\niotLF7G8toyxxlh2rAo9h5xAGh2AIn/eNJsxXMZdrsVnHmB/f7K9Sg/IYKSOhspKdzSQhpAgapvX\nHGiyZaXRmEOPPoexmUP7SvPj+wDco6SAfjVJkgcg0kP/L4/f01djfV0wa7WaADmUtFK1OJ6aAq5d\nc3sO2+2s3cPoqDgWpTF6sQh53vPEHznkvGKRI9d4/Lgo6CTDWK/bC7oyZKVc5tAn3MeHOTxwIOtN\nJq+jjc0YGAC+53tEawn5uwYGBDiXwKQIEM6cEY3skySTnMlzQAmkqdVEawl1lCUrPX7cjzm0gcOq\nW1Lo5oYwhy4WMGYri6oDaWzvzcYcPvYY8NzniufcI4/EkZX6tOChDPU5WRVzKI/3sY8JuwBgfia4\n5IIAHxz2yEqJCZtVBtLEZq4oslJVklo2OIkRSMPxJFHXWIasdKg+hCMTR/DM4jNY6ixhvLHNHAbc\nI7E2HlxMmQqgBpIB8rHk8dTNnq10i8QI+QJY9VjFQfW0yp8JYc5J92SfykpVNo/qc5TzhgaG0Nmg\nMRQxZaU9fQ4JstLi/VV1WqlJ2+KO/+njcf26kBlOTgJf+5r4T73R8GMOATdzqEoGJyZEPy7191AC\naQDgN38z/7qcVyzWJWA4dEhIntRCqAxZqQRAumELRCmOEOaQ6jk8fVpIPo8dc5+TtTVR8N16awYO\ni4yj9J+q49Il4EihyYtkEyjMoW6UJSs9elS8z40NmuewKCtVhwlkUJjDqsHhTqeVluU51D2DXLJe\n2z3y5JPAq14lniMSHLo+N7bjlcEcqgoLKnMYwrirqo83vjH/mrxH1N/NCaQJYTI48fNltxvw9dgN\n1gaxkVpkpZZWFkVpF6WHo8omSSkvBWjrmo47g2U0558sPY4YSGNjOOXv1BWonY1t5nD8KM7dPIeN\nrQ00B5rdY8VaI3fjwYfx8gFQ8ni+n1MdgHWxy3IeCxxq+uWx+xxSwq08A2mW15Z7vh97qNeW4zn0\nAZSbW5vdz3ts5pAcSFMElRWmlf4xgL9IkuT1SZI8N0mSfwrgz7a/v2vH8rIIejl+XOyQj44KMEBl\nDqngUJ0zOSlkfLJXIkD3HBaHCzBMTYkG8CqA4MpKbcyVa407nVaqrvE5zxEy4rU1tzxO+ikPHhTM\niTyW61xevpylygLCt/X2t9uPRQGHJlbOlURpmtfpCEZ7bEywhypgMYH6MmSlXA+gnLtTgTQ+4FAn\na7R93nxa40hwGBIIZGLzLl4UGwh33SU+N/0YSKNuvpnAoS9zKN+bztNte+bprgEnkIZbrJIDaTQF\nvMvfGBJkE1NW6htIk8IdiGKU4xGKY11aqW9aZpnMIcfzCZilpe2NNoYGBHP4+I3HMdYY6947Ib7U\nWMwtJ7G3alkpR2quzvUJLuJuGLm8m915Hn7KwXo1nkP1M+gDDuU8ik9RBtaUJSv1ZbPlqNpz+HMA\n/hrA+wB8CcBvQ7Sz+Hcev6PvhvTLHT8OPPpoFqxCZQ5lIXLokBscqs3sFxbigEMX0NOBQ24BaSrq\nKPK4splDV59Ddad/YkKA+rNn3UWu9KAWwaEryEZ6PuW45x43S1z0lxZHCCtnY1OTRGyQXL8uju+T\nVqpbI0dWuhPMYaxAmn6SldqeB5wNBCDb6Dh2TADFfmxlUZSVDg7m71GTrNT2vJNJpjpPsfo8KQ7d\nNaCwQkUA5VOs9rSyIPiLQhhA6dWSx6W0KfAJrQAiyEqVuWUW/rrimBKI4ss2AmZfWAg45wSidDZF\nOunRiaN4/Mbj3TAa05ytdAs1R7lZeXsVxrXWrbMsXyRgBxo+mw9cWa8qzbStUcscGlQBO+E59Amk\nkfMGagNG5QIAXFy8iJFfH8Hnn/l8r6xUM2+3pZVaV5okyWsL3/r09p8EgNxPfRWAT1lX0cdjaSkD\nhw88kIFDX+bwJS/JwGGa9iYsqjvbk5M0cCgT+DiAQWUO5+dpzCFXVkqRZ3GYQ+mDo85zMYfqGk+f\nFiFBUhJmem8ucGhjDl/0Iv1abRJWDnPoClKhNACfmBBSWNU7ygGHe8yh+Xhlg8NaLT5zuLkpNg0O\nHRKbTBcvintE3WjqB+ZQvScPHQL+zb/JP4M5zK261iKw9mUOqawQ23NY6I3FKah92DXJRPjsbMcO\nRIkdSAMEFNWe4ER3Pii+VHnui8eiFPAcNqPo3ZSjKyudOIrHbjyGAyMHcnNi9dkr0wdbXGfZzCHn\nesdkDrmeQ25a6U73OVSBrTdzWKsLWakFUH740Q8DAP7woT/EYG0wd6yQPodcWak6j3KsUM/h7xm+\nL4GhBIknHb+nb4eUlZ44IdoGyOKYyhzK3nUvepEozmSRWyw6inNu3syDQ21BsV2U+KY8AnZZaUgB\nyQGVPsyhZACHhrJ4eJ95FM8hIBJf/+7v3LJSFRzKRvHq+zUV1ZcvA294g36tNuZQ3k+6EVtWqp6T\n8XGx5vFsA9gITmxF9dcbcxja5zAWOJT3ALeViOkeuX49Y+KOHBHhXdPTItQJ6C9ZqQRrjQbwnvfk\nX9c9g1znH8jWWtwM8WYOK5SVUiVknIJaXaeUU1KKF46s0QRMADcTW5xLkdrKdRa9O1xZo22eKaCE\n5EvlAigGm2GK5e/KSseP4MbqDZyYOpE/VtWBNMz+lFVuIMRiDrfSLedGDkdqbmKld2sgjeo59PUP\nDtQGBNtombOyvoJX3fYqfPHSF/GyIy+LJiv1TZkFDKCyTFlpmqYnDH9Obv85kabprgWGQCYr/YZv\nEAmTsu0AlTm85RbgAx/IQJ5JWlqUlV6+LACojYFysYZyni6SX74W03MYEohShefQllZaPJe33SYK\naqqs9OjRrF9kUVaqmzc3J1pm6AbXcxgiK6WAQ8kKyWECJ7b7ZLcwhzEDaUL6HMYOpAlJIjZtckjv\n7NSU+JnHH88k0/0iK6X4dX1bWch5vj5r3bnkhI2EyErLKsTl8WQh4lVQR/QcuuSQRVBDKXK76+S0\nAPBMyzQF0lBSL6tqJSLn2WSlRyZE6lpRVrrTrSxKlZUyWbmtdCvXOoOyGaA7/xK82KTc3N6n2j6H\njPsfsDOHu8Jz6GAOW+st3LXvLpxfON/dLAHMigeurJSjzKAwvq5z4uM5fFYOKSs9fVr8+xu+QXxt\nNmngsFYDfvRHs9dM4LAoK71wIc8S6QoKCjjUhh8oRb8Pc8htiUBhQEzF4K/8CvBbv5X/XSGBND7M\nIZBdE5es9PDhrHdhMZDGFzBz00pNARkxwOHUlB84NN0nu6WVBZepLM6jrpHTSoELDk1gjcMuz85m\nmxxJItjDubkMHPYjc6gbnPMPmNdqYw6rDqTpkZVSA2kYhXh37nYhUqqs1MBayXm24kcns6LuwPsW\n/qbeg7bjBbE0FbWykPN0BbLscyh7G55fOJ9bYyzmkMx4MdlUzmYM53OaJEkPw8ZlDknyxEieQ5Ks\n1BJIo1vnYK28QJqf/sRPY741DyDPnnE8hy7msLXRwqnpU7i2cg2Xli7h4OhBAOHMYaisNE1TskXA\nJs3/ugeHkjms1YD/+l+Bn/gJ8X2qrLQ4TH3Gij7Fr341n6bHBYdaCZMGHKrFDKegs82jgEpTkftr\nvwb87M/2roEbSEP1HO7fL75++7dna7Qxh7VaJqujBNJwpJeu81+riQK9GJARAxxOTwPnz9NlpTZw\nGIvd7FdZqTqvnzyHIbJS2yZHkXEHslYmHDC6U8xhbHBoYw57JKwlJtXpWmBQWAKurFSVyHHYRnU4\nZaWGgs4X1FD6tQEaOR6lBUDSm1bqlJUGyENjtrIITSsFgNeeeG2uNQFX5hmUhBvB41imrFTOU88l\nt5UFJ9WWeh/rmEMSEDUE0lQtK33v59+L+x++H0B+7WV4DlvrLYw1xnB47DAeuPxAOeCQEUgjr7Ur\nJAzAHji0Dek5BIAf//HMS0OVlRaHqXm7Kit985uBj388X5joiqxYzGExkCYkWIYzz8YcnjrV+7vK\nYg7Vc/lt3wb8yZ9kINElKwVEYXz+fB7ExWwCLgt82+AwXhRwODMj3lsMWemzmTmM3ecwBjhM00ze\nWnYS8fHjtHn9xhzqNqh8PIfF4dr8YclKA4pVFZyQA2mYslK1EPFiDiPKSl0sWxHUuJhGOViSQQZz\nxQV5HCAq53GZQ62sdEPISgHg4z/8cTzybx7JjhWQhKubV9ZGR5WtLIDezwAlnTYmc0iRGerYbFJa\nqc5zWHEgjTxP//v8/waQZz1t/uXikPNczGF7o43hwWHcNnkbLi5dxKHRQ91jxWxl4cscUtheOfbA\noWVIWWlxUHsWUuepstK77xZfH344e10HMlytDUzz1MJlZEQAoEcfjZNWGps5lOf+5k3xlQMO1eLY\n5jlUi7nxcQHS1TXaZKWAAIfnztGi/CnFo04eSgGHvoyXD3NIAYdlMYe7MZAmlDmM4Tnc2hKMcpKE\nqQIo9/G/+3fAfffl5/kylf0kK+V6Dm3H017rCmWl1B5vIYE08nhc1kQOG3tiYq3SNHXKRHsCaUI8\nhwxZnQugc5nD4rWmzuPIeuU8XYHc3mh3m9436g3MDM/kj1UxK9rvrSzkOn0TJYgo3r4AACAASURB\nVGMxh9zQHArjbtp4qJo5vNkWReS1FRErn2tlYZGoF4c8v07mcKOF4YFhPGf/cwAgCnNYvAaUjb7i\nPAqgl2MPHFqGlJUWh43t4oDD4pxf/mWRkCpHWZ7DJAFe/WrgU58Ki5+Xa/TdRQfs5/L6dfF1dja/\nBh9wKBm3JKF7DovDJSsFhC/1ySfzGwqcc5IkAlDEYK4Ad2ImlTm8do0mK302BNLUatmmgs+8KltZ\n2Dao1KEyzrE9h8U1Puc5QgquztvNgTQhzGE/BdJw+q5xPYecQBoOODEBk610CwkSZ6sI30IciMsc\nujyR3ITNSmWlhqK6s9npykp71hgpIIY8L6CVBVvGzQyyqQwcMnsxagNpCBtNxh6ammfeYL0cz+Fc\naw5ABhLVDSSbRH0r3cLfPvW32Eq3ckmwLilqa6OF4cFh3H1AsD1TzansWJrnFjmttOAfp8pK5bw9\n5jDSUGWl6rCxXVzPoVpMvPOdIh1VDl1B4QJrcp6r6HzFK4B/+Iedk5WazmWaivYQz31uGDhUC1+q\n57A4TCBvdRUYHhZ/v/NO4O//Xvzb1oJEfR+mERKIogNRoYW/7NcZgznkyEpDmENOSIxk2XzbUvRj\nn8MiODQxebZ7hBOsJOftBlnpTgfScCRkbFkpMZBGV1AncPtUOIE0Nlmj6byYgAmFBVTnpmnKYoWo\n82K1sigTQIUE0rhkpbo5VQJfLXPLaN1QBXOozuP2OWR5DgNaWZBSTjXXzXReymIO51vzmG5Od8Fh\nrpWFpWfqg1cexGv/x2vxB1/5gy6wSpLEHUizLpjDk9MnAaDr8QuSlSbhgTR7zGGk0Wpl4QrqsBUw\nHM+hrsG56hc1MUIumaF2l7pQ9Jw4IRivnQqkMTGHy8vid952WzhzKI9vOhbFk+RiCO68E/jMZ/I+\nSS6bymXKypKVzmyrgkKZQ5tktizmsPj+QhhH38/bTvY5XFnJ1iDXbQPnMZhD3bzdyhxSNt+4zKFO\nVjqQ+PtNOMCL3H6hUNClqTvhrrhObkEtB8dzSN2Bl0yBLLAoAQ1qUZ0iRYLEOY8j9aw6kEYHRtM0\ndRafJsbFJpPeCeCrZW49WzdwvYMhslJOKwsym8cIqdK1ZOEwh1IpofvsjAyOYHV91fo7OWOuNYeT\n0yfzzCHBc3hl+QoA4OLSxXzCKUVWOjiM77zzO/G5f/m57vdNm1psz6FvIM0ecxhn6EAbUL6stDhM\nzAknoKQI1m6/vdxWFtw+h/PzIjBn375w5tAVEBOjyL3zzuxnXfM4xyurP598TSdbLjKHMpAJ4LWy\n4Caq6kBl2eCQwzjqjrUTfQ6vXRMsb7tNk5VyAoHkGjlybNs9YvqMhgzO5k9ZzKFRVupgDmMVq+RA\nGq6slJFWyg2k0RVnlLYUaoFGlZQC+aKay9wCbqam6kAa3fmngGZbzzYTsKnac8gGowEy7lwBD57v\nth89h9qWLAzPrW3zYGJoAkudJevv5Iz59jxOTJ/AQmehu/FB8RxeXroMAFhoL+TOLYU5bA40Ua/V\n8fKjL+9+P3ZaKen5muwxh9GHqaiwyUpthYgNHNqKl1jR+kAvIyETBg8eNM/RzSsOroTSBLTl7nsR\nHMrUUY6slAvWKKBydBR4xzuAN77RPY+TDklhoTjMoWme+t5OnQK+7/uAt70te50jK5XH4gDYIqis\ngjn0nccBlLo1AnzgBQAf/aj4eu4cnTn0lfVS1tgvslLX5ltsWamNOdRu2G25PYchslLfhLuQVhbF\nQBoqI+dbMJmACSUkQ2UKKGBSXafq3eF4PgH3jv9OBNIUzyW1lYKvf4p7b+3WVhZejcoLaaUccEjq\nh8lsyaLbQOB4bm0e64mhCSx2Fq2/kzPmWnM4OHIQQ/UhrKyvkD2HV5avdOWoKuvmYg7bG20MDwz3\nfN8EDjmtS6iy0lwgjQdz+JG3fMS8FtJveBYPE9CzMYc2EGUCNSaGUg5ulL8rkAbIpIKve515DhAm\nK+Uwh3Le/v3AjRv5NXBlpbY1cmSlxWv93vf2zotVVJeVVgpk97N6D6prnJgAPvSh3jm+slIge2/q\n+fbx88lz4AO81HX6SD05zKF6rLI9h6Zn0AMPiK9nzojNlRjMYT/JSp94Qqz3jjvsPycHp5VFmcxh\nz4ZdSSESAA9Uclma4vGo/QO5gTQxduCpawTyLKAXWKiqlUUA48hhoDiFrgmsuTYRQoJsdkMri+I6\nq+xzWHXYkW1DZrwxjsXOItI0JW0sUcdSZwnjQ+OYbE72Aj2L5/DK8hXctf8uLHQW8myjiznclpUW\nR+y0Ul9ZqY9SQvoldWOPOWQwh7aYdkqfQ93ggkNKIA0gglR+8AfNc+Q8X0YIoHmnbIWnSVZKSWqU\nc1zFsa2Ys81znZOYRTVHVpqm4o/rGRszEIVyTmJsdIQwhxSpJ4c5LN7LO8Ucrq4KJvvMGVogTQhz\nyLn/bemoJumxOn7v94D3v9/8enFwAmm4nsOtLXerjp77n+Ad4TKHxWKcFD6xLU9MFR03p8il+J8A\nvfRPzvdNK6UcUy0GqWsE8iwg1e9TZA7TNO36FU0jVvsF6jxTAe+SOtv8U9E9hxUDZo4cW3c87ueU\nE1Il53E2mkjyUK53UxNkY1MENOoNtDZa1t+rjrXNNa0svfgzQ/UhTDWnBDhUgJUtefTKyhXctU+A\nQxXUOj2H24E0xRG6qRV6b1HuK8rYA4cG0OBiDk2FAddzyGlRANCYQwB41auyNYekjnJBpY05nJgQ\n7SHkGkKYwxBfZFXMCRAvkEbOqRIccu4TatgLB3hxARsneTQmc8iRHsvRagEveIFg2GLISquWmrvY\nw8VFYGHB/HpxVNnKQs4zfeZ055KTIBrCHLoKuiQRrSBCCxHqzrau8HQFooSklRYDabw8h4wejup7\nowTZyIJaBefcREmu9NI32EcdtqRNHfCV7QFsI3YgjS/jGMIc+rI7wO7wHJLvSc8gm4mhCSy06Q/4\nn/r4T+H+h++3/sza5hoa9QammlOYa83l2t0MJOZAmrnWHE5Nn+qyjWUwh3LDyPf/AKpEVJ1HScGl\njD1wyGAObbviXFkpN5CGyhy65gDlyUpdzOHoqEguVddQdSANtzjWzUtTO7MA8JnDkPCVryfmsCxZ\naT8xh9/0TcBXvhInkCYkWEm3EZCmvARdORYXxR/qqLKVBeWZoGUOPYMduHI1KgPCTV0s+luoslJT\n6qIJRIXswLMDaZRrQGaSCteNUlAnSYIESS5chpsoGcIccmWlZXgOY0pmubJSKgvObYGRk5WW2cqC\n4Ys0qQk4gTSuTaPJ5qSX73C2NYtLS5esP6OCwxurN3LnyCYrbW+0cXjscDeQhuo5tDGHxXmyTZBL\nRqv1HBI3HuQxfZ53trEHDi3g0FS82Ap/ExAqM5CGwhyqgyshC2Ecbczh6GgWy88Fh6GBNCGyOlN/\nSttzgOsd7Ic+exRWKIZEOgQcUqwM3ECaqjyHtg2q1VXg3nuBBx8UP1MWc8jZVJHPR9/7Xx1lMIcx\nwaH3/V9iU24dyCMVuUzmRD2ej6xUx1zZ5E8haaVFDw45kKbAHFLeWy2peYO87rF8G5XrklGJoSGc\nlgjctFKud3A3BNKEtLKIEUjDZQ4pagLdhgWXcXQxhz7gsLXRwnxr3vozKji8unwVQwPZfwimQCxA\n9Ow8OHqwK0WV59YmRU3TFO2NNpoDvX3wTPc/h12mPoMa9UYugMsVfkYZX/fg0BRUwpWVuoCQaYSw\nLTGZwzJkpS7mcGwsDBxSAmkoTF4sOarrfJjWyUkr3SlwWFYPxxjAl9IflHu84r3M7XOYpmGBNK0W\ncOSIaEFy9mweHErmTh1lMoemzRHbqJo51D2XXfexaZ0syThDHseVlXqlJ6rghBjJnwt78djZLhb+\nriI3VlqpVyBNwXPIkZWWGWxSlAJT55nOP8XzppPW2cJUKg/b2elWFjvQ57CscKuYKae254IvOFxd\nX8V8mwgOh6bw9MLTmBya7L6mu//l6Gxm4DDnObTISjfTTSRJon2PuueWj5qD8yxv1BtY2xR+NkrQ\nEWV83YPD2LJSU9HjKlhDAmk4zGFMWWkM5lDKSiWIk+twpRrKnwkN5AiR2vqef7lOrox4p5nDMlpZ\nAGHMYTGt1AVOuMfTMYecPoeS3eRKL1dXgeFh0Z7m8uXsOiYJ71kSkzmMBQ5jMocmWSlnnbbnv5zD\nYQ51LSm4rSy483zbUlA9MZy0zCBZKTeQhuM55MqBOcyhKTTE8f5055IrawTswDKEOeSEJHEZx1it\nLLigst88h6Z5FJa+bObQCxw2t8FhMw8ObczhzPAMOpsddDY6mefQIiv1Zc65z0nq5psKDvcCaSIN\nTisLjqzUVUSWHUjjmiPncWSlocyhTlYK0NlDdc5OeK50zIkLHMYOpHENEzh0+bSqZA65wLd4f7mK\n95DjxfIchrRRAARzODICTE0BFy5k7WoAHittYhx3kjn0AYdVtrIokzmMkVbqlbKpBqmk7sCE4vGo\nYS+cQBRdaIucRwFDHA+OysyR+xwWmEMvr+IOtxtwMVe2tFKfQBrKGrkhSVpwQu3PV2Eri+I6Q1pZ\nOL2pOgaQUO5zpc46psy2xvHGOJbWlpzrkWN1fRVzrTnrz6xtZeDw3M1zPcyhERxudjA0MISxxhgW\nOgvdZ4tcv45xdN3/nM0ROTf3LCGCyhxzuBdIE2dwmUObrJTDXMVKeASqDV+hHI/jOQTo4JAiK+Wc\nf7ke33PCZSQ44JAra5Tr5F63fg7b8ZGVhjKHm5s0f2NscCiZw+lpAQ7HxvLzfM+/ZDFjqBD6UVYa\nM5DGxUybAmlITEaEPodegTSBx6P6+UyeN1sRkyQJmzlRQY1XWmmSZw45rSyoPiHOZkAIS8YNpCmy\nJ/L4pmNyA2LkXA5g1nq8CBsIlTKHtV7msDRZKTNRtTiPGmTjm1baHGiivdE2vp6mKc7On+3+u7VO\n9xxONidxfuG8F3M4VBfgcL413xNkY2qn4wMOOf0KAfp162EO9zyH4YPDHHJkpRRZV4yERyCs/YKv\nXErOC0krHRsTstJiyqEPc+gKpKEwJxw/Jef8m+Zx2TUOEKKsc7cwhzpwSJWVyuNx+0VSj8UFh7ZA\nmpGRDBy6mEPKfcK5l02yUsqxKMxhkck0DU4gTVmew6oDaXQeKE7vQW5aKVUu5es5BMyFls8870Ca\nqjyHkQJpuOxaCHPlChLisJsA75z0QysLrueQAvJ0EsWqPIektFJdII0DDA3Vh9DZMBd3//5v/j1O\n/ZdT3X/7ykovLV3yZg7HG+O42b6ZW7dJWmr77BgDaRgbRtSNvj3PYQmDwxy6GiDr5lHASQyfFsD3\nDroKSG4gDYU5XF3tXTeHOQxhbjmyUtP558hKyw6k8ZXV7dZWFlRZqXo8ar9IXSANB5yHsmuqrPT8\n+XDmEOCpEHTHogBm23vrdDJPZtu8uZwbXFkp13P4rAmkCUxdLFNWKucVd+6paaXsQBrlvVHORw29\naaX9GEgTysDK4ZKtcQNiACZzGLGVRZnMYZVppTp5Ypnn35c5HBoYQmfTXNx96tynAKArKV9dX/VK\nKwVAB4fbzOH4kACHpTCHHs/k4rPcV1a65zmMNNbW9MWPrXixFQe7wXNYq4kd+a38BmRpoRUu5rBe\nF1+Xl3ngUL0esYEvJWxHd/4pRedu6HPICenhSmZ3MpCm7GNxmEPbs6TTAZpNPXMYIlmOwRxyNjnU\nsbQETEzkU4xdgxtIU5bnsOdZzmg3UIXnMJSpDJGVUrwxOnDCCaQhg8MCc8hp0+HlVYwUSMNtZcFJ\nK6UECXHWKNfpnZYZ0B+xylYWxc9bmX0OuS0ROMwVhzm0yUq30i08dv0xAOj+TGujhZvtmz2suTp6\nwKEiK9Xd/3L0MIeJmzm0fXbk+eLIenXn31dWuuc5jDA2NwVI0oGGqmWlVe72cxMNbbJSLnMo542N\nATdv+oHD7/5u4OMfp8lKQ4CvL0tAKTq50r9YoS2UdYbISqtkDrmBNDrmsMxjxfIcttsCGCaJAIcL\nCzRZaZXMYYisVEpmm016O5syA2l0LTB8N5qoqX8xIvIrYUCU0BZq0RlNVurZysIrrbTgOSwr4dE0\nr+pAGras1OJp0gUJlckcsmWlO9DKQj0eFZzHAodsWSklrdSXObTISs8vnMfE0ASmm9NobbSwlW6h\ns9HB+NA4FtrmlDIJDk9NCzlqo579h6BTLgDonqOB2gDGh8Yx3857DrmJycV2FuSQqlodG6k/qC8y\nh3vgMHDItEadlCxEVsr1HMYACwBdssYBh2Uwh4CQli4s0MHh2hrw0Y8C//iP8QJpqkwrjRVIs9fK\nove9UaWesZjDqsGhDKMBhKwUiCMr5bDgse5jdbRa4v01m3RZaZXMYb8F0hR3xb0CaQJlrNSdbVMg\nCiWQo7hzX2ogDddzWJE8Ub7uK2PVFfAk5lbTs83laZKpoyypLcfzxpWVhrR7iBAcRZaHMuXYMfyU\n1EAa3wCWoYEhI3N4dfkqbh2/FcODw1hdX0VrvYXmQBMzwzNW36EEh/tG9gEArixf6b5mAnlSUgoA\nY42xXs8hQ1aqO54Pc8uRA5cRSBMOL3fxsO02u5hDX3BYlufQVJxxUk5DegGGpJUCAhz6MIef+5z4\n2mz2ModccMiRUIaklca43mWmlVYdSBPLc8gJpOEyh1xZaYjnUPoNAdHnECiPOeRc61DmUILDjY1y\nPYfca9BvgTQ6kMfpqcUNpKEALx3j5SsPVef5trIgB9KonsMSfVpyjSG96Gr1DCiW1crCBM5djJe8\nt+R5L5MFLwIoyVi6enZy03pDAoiK4LA50HSuUcscJg5wmPCCTaIF0hDSSk2eQynzHBkcwer6KpoD\nTYwMjmC6OS18h9P63ynBIQCcfcfZrrwUsIDD7WMBor3GjdUbec+hoZWLa2MlxAceLCvdC6QJH7aC\nQhZSvqmeuiJEJiGWFUgTiyUIYddsxZLNuyYLz8lJYHY2f15t4PDRR8XXubneQJrYslKO57CsPof9\nkFba78xhmbLS4ue7bFmp7vy3WmJTBACe8xzxtUzm0Pf+525OycFhDtfW/NNK+7HPYeWBNIVCMIG7\nL8tAkq2TKtkMkccVd+4pTGCj3uAF0hQ9h0Q/pa9PDugF52W2INFJPUsPRKmohyA7dTckrTRCIE1I\nWizLc1iW1FkXSBOQVtreaGOoLsBha72F1fVVjAyOYGZ4xtrrUAWHJ6ZPYHo4Q5EU5lDnObTJSm0b\nK9zzvxdI0yeD0xsLsLMFujkyIt+2kRUzkCaEcSwLQLmYw+lp4JlnBIMoR6NhBodf/Spwxx3A/Hy+\nWIstK+UwJ1RZKfd6Vx1IU2wnUBZzGNLKIjSQhsrAhqSVqmsMCaRRn1u33JJ9T44qPYdlBNJI2ayv\nrLSqPoecQJqqi9wyW1lwZaU6z6GvD1POozCOHA8OK61UI6HkBNlwGSjKPJ3UkwJOOGml3DXKeaEh\nPZW0IInUyqKsPoexPIfsQBpCWml7U/9w72x00BxoYnhAyEpX11cxPDiM6eFpkqxUN0jM4bbnUH0m\ncT2HnGAf3TzqdesJpHEwy5SxBw49pUiAv6y0LCYPiFsIliW9pHgOp6aAp54SIFEOG3P4+OPAK16R\ngcPQQBqbn9KXJeNG5HN60ZUJDmVwka9sNhZzVSVz6CMPXV/PADN1nmRh5bwQz6H6eZMbTsvL9nll\nppXKYC85qLJS3ecU4DOHZQXS7ApZaQSWJkVaWp9DnXdqfXPdWRyb0kpdoLJRb+RlpdRAGqXQJUtm\nQ7xrDDlwLIaZy9xSQU2sc+IrK/VhbtmfmwhBNpTzLz9rRcbXd1MlRCLtOpaJObS9t+ZA08gcFmWl\nrfUWhgeGM1mpYdjAoc4XCeSZw4mhCcyuzubu7VjgMCQt1ldWusccRhiUgsJUnPmklVJ20mM2ri6L\ncbQBL1d6H4U5fOqpLGADENfGFAp08SLwwhfqZaWxAoGKa6TOK1tWWhVzCOilpSxZXYnvjcvmqcej\nAspaTfyRbWB85qnXgBtaBPRet0ceAX7qp/Lzqko+Lp4PeawYnsOhoXIDabjXwHXNubLSWEEXZe9S\nq8U4lZULaqVQ8LxRQJtaLHkF0iiFrs97q7pfXoxU2zLTMkP8rN7MYQhLWaFXtAhiKQxsLan1ML5s\nWWlJbLZu42dziyArNXkOtwHbyOAIWhutLuijBtKY1uhiDmeGZ3Bh8QLJq+hi3YubKj7gPFRWuuc5\njDBMPQ7l4PR50wEhbupovzGHujlyQ8smmS2yJnIUmcNz5/LgUHdO5Gi1gKNHe5lDE8hzAeYQWWlV\nabFyXj+AwypaWXDDdijnv7hOKqAsHs9nXqORyT+57DLQe/7vvjsLqAH4z4RY/tlQWakM3FGZwyee\n6H1+qIPLHHKugYstjsUcesWfc/xFEaL8qTvb3LRMLjjhRrsP1ga9W2DEZA7L9sr5MlectFKgYs9h\nCANbkaxXrtP3/Mt1Fp8LfZVWqtn42UwJslJDWml7o42hgaFuWun61joG64OYbk6TPYfFoQOwQJ45\nnBmewVa6hX3D+3LzYslKOZ5D6rO8jLTSr3twyJGV+ray2IlgGQ7jyGEOKYVgrSbAo8ouAHrmUJWV\n2qRn7TZw6639ISutmjmsSlYK6MFhvwfS+PgAfWWlcp48Jz7zBgfz4JAbSFPV+fdZZxHUx2AOJTi8\nehW46y7gne80/76yWlmYGMcqAml8JJvsQJrQVhbENepkXSRZKTOtdLCW9xxS00oH61nTayrj2OPT\norIEEfpMhsyjgnNWWqkGQFHCjrjM1W4JpPFlbk3zqvIcUluJaPscWj5zLllps97sykrlc2J6mC8r\npTCHEhTKVhi2ea5rV5TDUzcsiq0zqBuEPZ7DPeYwbHD789lkRbo5XHAYIkctY56Uj/lKyAAzyFCZ\nw+XlXubQVkDeequQla6sZGmNtkAaXylYcY2meTvJHJbZygLgM4cxmCtOEuvWltiIoJwTjqwUyH/G\nfeYVmcNYstLiiMkcUoCeDtTHBIc3boigqt/+bfGabnAsAlUG0lCDHbi7zVW2sogBoAAPWWmFaaXq\nPLKslOHTAuLJQ0sPREnzHwAO41vmewuS9e5wIA2HOeSAQy8fJiMtVvfZtjKHdTNz2NnY9hwOiLRS\nCfqmm+5AGgn0ioOSVjozPAMAPcxhcXNEvj8v5tCjX6F6PCoLuOc5jDzW16sJpAnxAJYlR+WAExlQ\nUgZzJRlDH1npoUMCUM7PAxMT5vdFWadNVurL0nCZE25aKSctE6iOOZStXBxtp4KYQ7lGH7AWizn0\nAYfqOrlppa65IeoF3T3pq0KIISuV4LDTES1uXvxi4PBh4Gtf6/35NKWdE99NDtM6uYE0lN58HL9J\n1UE2Q/WhPCvHCFEBiE3YDYE0vrJSaiANh3Gs1+q8hu8BKZtcpthbVso8/+ywI24gTYXN7Lnz1E0V\ngM7wxGIOy2rLwmplMWDxHG4KwFaUlVI8hyYgRWIOR/yYQ+9AGsL5V58/lOPI0ag3uudzz3MYYXBT\nNjmy0jKa0svjVelV5CZKupjDfdufR4qsNE1FATk6Kpp/X7gg+iTKOZxAmtgsDfd6+24G9KvnUD0W\npZWLnMe9tzgewBjMoQ+ojOU5rCotVs7zvZdjy0rn5oCZGeDUKT04lN5xl+9Zd/9zrkFZstJi0enT\nJiKWV9FXwuSSj6lr1PXZ40b5O2WlSisLn0AatYCnFma1pFZ5IA2HqSxeb6p3UCsr9bxuZbeyqDKQ\nhjtP9bMCdFaIAw5DfJHeaaWMVhY2WWl7o91tfF+UlZo8h5tbm0iQGNdqAnmqFFUG0VA8hy5gz2Vu\nVeUCQAd6XI+1beyBQ08/mZxnSyvVBdKU5TmsGlSWJWu8997se3LYpJ4DA+L16WkRZCOZQ26fQ10k\nf5oywycqPP/cRE+gOuaQC2A586hhNMV1+gbScJjKfpeVhjxLypSVSnB4xx3AmTO9P++SlMrjxWxl\n4UpnjhJIw2wTUWbDa6A3CZSyRm6fvRhppT7FUhH4cmSlZUr4gECmWDkeF5z3XSBNxd7NEOawSllp\nVT5YFnNIkZVup5Wub61nslKD59DmNwT0oTlA3vc8UBvAVHMqxxzqPM8Akzmkeg6LslKCRLSMQJpw\neLmLB6XI9ZWVxvQc9iNzyPEXAW7msNEAHn1UJJDKYWIOZfEIZOBQZQ45slLpU1PBoCwCbYzEThTi\n/cAc+jAn1DXGYEVDmEOfYJkYaaXU0KKiJJclayx5w6IMWWm7nQeHJ06Ith3F4QqjAfibI5y00miB\nNMyEO69AmkIhmLjoffCBl2R45HuiFDGmQBqS55DR53CwNojV9VXycQB+IRgjLdZnHktWajj/nECa\nssBJSCBNeysDKFW0smitZ6bpKj2HPj5k340O3QaCa2PLJSvdX9+PWlLDtZVrQi7qkJW6wCE1WOaH\nnv9DODF1gjbP8uwK8RxyZaWxA2m+rsGhSx4XS1ZKDZapMpAmViEYE5w897n5100sYKslWAVAFI2P\nPBLOHMo1qteWk9QI8Ivj3dTn0OXv4t4jMcAhFeSFtLLgpJWq4JDid5M9BIvH6DfmsCxZaaslwmjW\n14XsfP9+4T8sjp1gDn0DaTj92siBKLpWFkRwos5LU5ovjCMrBXrBaEgrC9cxOQygnOcLKqtkyUKP\nxwEZxUAgMnNVUXuPEO9mDM+nj6x0cWux+2+qZLDv00p1slKHosCaVrrNHMqNGsnujQ+NY2VtRfv+\nKeCwyG4Cvc+g933H+3rmcZlDlQH08RwWpcdU9UKaptjc2twLpIkxKDvwMQJpuDKrslpS2I5XZqNy\n3+LMFEhTZA47nXDPoTyeOpfCJnG9g/0m6zUNjqyU0/dON69fA2m4aaXFVhaU43Gat+80c1iWrFSy\nicVBYQ45II87b0dkpQxZI7eAb9QbWNvyk5UCevDLSSulBqlwW1n4ehWr3KYMmwAAIABJREFUDgSK\ndTwuOKfMC/IBBjZh3y2BNNQiXgcOfcOtSu1zyGhlYZOVSs/h8OBwV1Y6WBtELalhsjmJm+2bPXOC\nmEMPBlCdZ/sMFBl36jNZff5QjiNHkiTdUJq9QJoIgxtI82zwHMbyF5UJTkwS0XY7Dw6B8LRS3Rqp\nxWOs68ZJte1H5jBEHlo1c8gBeUXmkJNWSjn38li+QSoh6oUY80JlpZIJHBoSn/WFBbH5I9lE08/b\nRkxZadmBNDK0hQq8QgrBGIE0PrJStYikFDGDtcFezyGxlQXXc+jbyoIrIQuRXsaSlbrAiSmtlOJV\nDA3bkZ8Dl9SZm97aD4E01PvLdx43EIWTsslhDk2gC8jSSmUgjQr8RgdHu7JvdXDBoesZFGseVc1R\nbGXhIxEdHhxGe6MdzXO4Bw4ZzCEnrbRMcNjvLAHAZw5dstIDB8RXyRzq5qQp3ztVdcpsGT3l5PF2\nMpCGssYQVpoL8ritLGKklYaAwzISdLn3ZOxAGvn+mk3xWZebQTZwSGEOqwqk4cpKi6EtPkmgMWLr\nOeDQh5UrAgYOyOgek5BWur61LqRWPmmlDMaRG1vPYWnk8arqs2dKKy0zLVPO85mzlW51waRPn8lY\nzGECt1eXG0hT3CChbMiw78lYgTSOZ1ex2bs6ZHuJXFrp9ibG0MCQVo7KBodb62zmsAzPoa6VBRXo\nSeAcy3O4Bw6ZzKFPIA3Xc8iVh5bpVYxRwMvBieQH8rLS7/1e8dXGHFJbKXCL3Kq9ov3uOQyRh+5k\nIA2nlYXPvFjgsCxZaayNjlDmUJ6bsTFgZUWAw2bTDA47nf5iDrXPZEZIDJc5JB8r4ctKZYEWIiul\nyhqL4EQtFk2jltS658VHZqUyh1RWtEp/V+jxcuefmDrKTivlsnLb54Q6R26q+M6L1QLDR37MYYWK\nQGojLdFzyAikCWEO1dY2cnRlpQPDaK23cgBuqK4PsgmRlVoZwCROn0NquJj6/EnT1GvzbWRwpOvJ\n3PMcBg6OPAtwy0pj7dqHgMoyPW/cAj4mcyjB4YteBHz0oyKsQh6HU+DqjhcC8rge036XlW5tJ9G7\nekZy1hiy8dDZ/v9iJwJpOOCQIlkuHksOX88nUK6agBNIU6/rVRlAdl+OjwNLS25wyGUOuZ5DSlqp\njjn0ZQGpzKGOAeEE0pTZ57C7zmIrBYLnkCNrBLJd+NZGC8MDw6Q1qp6fja0NDCQlgkNukEpAWqlv\nKwtjWqljHht4KeeEKoUszis9kKbAHFILeG6fw6LUkCUrDWhlQfE3+jKHtaTW0x9Ujs5GXlZaZA51\nXkVnK4vE0srC8gwKCaRhhRYpzx85h/oZ6J6vPeYwfPSbrLTKYJOqPYecYA1TII3qOQSA7/qujBXk\nvi+AJyutOvxjp9NKKUEqVbeyaDYzcFiFrFQFef0oK91p5pD73JLDFxz2I3PIkZUC+aKCzRz6BNIE\nykp9JJvFAk3tMWYaJs+bT7y7ZCIoQ5XwcWWl5D6HFffnY8tKC/I/KuPLkmwymMPi8coOlim+NyrD\n3MMAeshKfUNKuJ43blps8TNKeXbp/MRAr6xUBX6mlNN+C6ThhosVU0d9QN5oYxQr6/o0V874ugeH\nnFYWrrTS3RBIExOcUD1vvsyhTVbaNPxfX6tlHkN1jRwAVabncLfISovXzfWZkXOqDKRRwaGvd5CT\ncjo0lGcAuWmlFHBo2lTxVTxwnwllBtK4wOHYGJ05dIHDWk2cA/W50G+BNEB+F57sOdS1smB6FSne\nqaGBobwvz0NWWmRcykrLBDKJVnujjeFBGnPIAb5VFuKm43HmhbQSKdNzqDKAHMlymcmoxWMB9M9A\nEQxR5c4cUNlzj4B3TkhppTpZKeHZZWowLzeNimmlQJisVMdSuj4DNq+ia556zajP5CRJutfblwGU\nYLq1TldK2MbXPTj0ZQ7T1A4adsJzuNMsQYjnMFRWWhxJwmeguPI4bp/DGDJin7RYTiBH1cwhZ55s\neQDwvYMhzGHVnsN+63PI2VRxeQ4HBgRzuLxMYw5dstIkyR8zTekbHRxwyGUO1cLThznkBJvovFNU\nX0wMWen6lts7qGOuqJ4auc7WRovOHCqFODettGyQFyPsBaC9P2NaKeG6VeU5BHiyUvYaNb48DsPM\nDaThModcqbPrvcnfK4O0ANqzS/fZBrL7q4pAGte9HGuej0Rayoi9mcPBUaysrWBlfQUjgyPkeaax\nBw4t514HaGSxZAo34XoOdwtzGMJcxWIOXR6jkDVyZKUxPYdlJEMCfFldERz2I3MoWx4AYR5Azjxu\nKwsfz2FVgTQhaaUx+xzK91eUlcr00uKgMIdA72ZAve4fUiXXZ3t/obLSbiCNh+eQU3RypU8xZaVl\nBaIAmX+nvdEm76T3JLGW3EoktN2D97xiWiwFnGsCgZzMYQRf5Fa65Wxjoc7jpJzGCBIKkZVSNjli\nMIchnkNuf0TKxoNOVirf38jgCFrrrRzwM/VHrLqVhesaFDdVfFhw6U31SSoFMuZwZW0Fo41R8jzT\n2AOHnsxhWZKuel38nBrexGWgquyPGOI55DKHnFYKHOnlbpADV+E5VOf1K3PIkZWqMs+yg2WK88rs\nc1g1cxgryEmOoudQysiHhsSc4ueNEkgD5J/nlPtYzqlSVpoLpCEyh7WkhhRpLsiDUqwWwQl1HgdA\nAQZwwglESf2CPFrrHsyhUrBWkla6g7JSrqyXct3YnsNCIBN544HBOHIklHKNxWAfjqzUxzsbgzlk\ntVdh+mcpbKpJVirf3/DAcDdgRd5vzYFm1LTSkFYWvp5DH+ZwbXONxxyur2B1fRWjg3vgMGhwgB5l\n15jjOUwS8TO+O/A7wRxyethx00p1zCElyCYGOHk2B9JI/5Xr/XGYQ66sN8RzyJGVcpg8OU8Fo2XL\nSmMw7lU+E7ifGznk+2s0xHNxYUFc4yTRs4eUQBogv9ERAs5dGxDRAmk8JJvc5u09RW6JslJuK4sq\nA2m455HF0jCZwxBWzreVgimttDTPoQIyvDcefNNKuWmxGiDEYQ7JrSw0gTSuz1xMNpsrbQ+RlQ7U\nBtCoN7CZbqK10SpVVsryHHoyjlRwDmzfJwxZaZc5XN9jDoOHa/fY5B/0BZRUdo1TVMfsj7gTfQ45\nstIymUPfIlcHzndDn0N57l2qHR04pMT/V9nKQoKura1qmEM1kIYLDqlsFyflN3ZichkhVS7PoXx/\n4+MC/MkAKp3v0Ic5jAHOqwqk8SkOhupZ0USdVyzgubJSnwK+yFyVJWsEsqLaJ5Cm2Mqi1ECgAOYw\nVlophQHkppWGet64kuWq+0xS75Oi1JAqG+TISosbCF4MYIT2KiTm0CIrrSd1JEmCkcERLHYWc4E0\nOllpZ7Njb2VR29lWFr6eQw5zmJOV7jGHYaMMWanJp0gpIDngcLcE0nCZQ9+0WHksjvSVG6wRw6eV\npuIPxQMVQ1bqk5bZ760skkSAg06HzxxyA2l8GEcVjEofnWtUKSvlhiRxPm9UcDg2Jr5K8Dc8nLHE\nclCZQ/V6hzCHnEAan+LMV1YK9Eo9ycxhoKzUR/oXMy2zrEAaVcJXdiBNiOeQU3gWWcAyA2lCejjm\nmEMPVrrr1fVp5RIo603T1AtUcmSlRRBF+czFuie5XkXKRpNLVgoIwDPfms95DtlppYVejPJYXFmp\nzzyO55C6CSbHaCMLpNljDgOHawfetGvvkpVyCjOAzxzuZCBNmZ5DE3PIkZVSwTknrTQWA5gkbnAY\nS1bKBYdlB9Jw3huQ+Q77PZAmFjisos+h3LAoQ1ZK8RwC2Vf5uTAxh1RZqa+MWL1mclA2p6oMpAG2\n5VabnsyhJr3SV1a6kdILeJ3nitPnkCrHGxkcwcr6inefwxitLMpOy2TLSlVZIzMQiOt5o4TLqMwV\nlckuHs+nlQjnPKobCBKIUt5bSJ9DTiANJzRHxxxyvYoU6atOVqp+7sYb45hrzZUmK+W2sqjKc0jZ\nBJNjZHAEy2vLaK239tJKQweHOeTISkP67FXVm4y6zir7HO5EIE2MZt5ln8cYrOiziTkEMt9hiKy0\nbM+hZDeBMHDIkjUyNowkk83ZsIjhOQSA0cIGqA4cUlpZADzmsHj/A+6NBJOs1Ldg9WEOubJSX58Q\nIIoXCUR9Cvgez1UAOKHI8aaaU1hoL3j1/Sp6DjkyQw5LDISBSmogCieQhiPr1YETcu9HhQH0kZV6\new6ZyZy5DQSf+z+kz2FgIA31XtaFVJXGHDrSSgFgsjmJ66vXSwukcQGwEHCovjcv5pDpORwdHMWN\n1g00B5pkIGobe+DQUyLqmqNrthziOeQyV1UFqZTd57DKQBoO8NoJeS43EGg3MIdccCjbWYQE0pSd\nVjoyAqyuir/7gMOqAmlCPje+vTepstKZmfxrocyhr+dQvUd069ONnQqk4bRgCJaVehTwWs8Vs88h\n5ZhTzSncbN/0Yw4LnkMuc1hmIR7CVPYwt47zbwqkcYFzjgdNrlH13Hr10PRNK2VKXzm9MIvz5Fwf\nebQ6zxcckhOMdcwh9V72ZA4pstLJoUncWL0R3MpCftZStR0A+Myh77wqPIf7Rvbh6ZtPR5GUAnvg\nkOU5tBWRSdILhJ5tnkNuARmrz2FZzGGstFIOqCw7tGW3MIchstJQ5rDsQJrRUWBlRfw9lDn0ZdzL\n3LDgfm4o4HDfvvxrOpknNZCGyxwWj8dRPHBYIS/msEJZaT2pI01F6wzfAr6YlslpZUFlXCaHJrHQ\nWfALpNnhVhbcABwq48VhDjmyXl16pW9Ikm8rixiBNNTzqMpKfWTV6hoB0I9XAJWUJFDfaw2E+WCL\nbWpC00oBYGJoAmuba8Gy0iRJet4bsB1Iw/UcWoB98bPms9Gn9jn0AYeHxw7jzPyZKGE0wB44ZCWP\nUtIaq5LVVe05jNXnkNJKwRZI45PWWHVaKbeZfZm+vJjMYVlppSHMofQchngHOYE0PuCQwxyaGPcy\npO0x26SUxRyqwFwOn1YWvoyvTlbKUTz4FKwc5lCVlfp45Tiy0iRJumC08/+z9+ZRsl1Xmee3IyNj\njpzz5Zs0vSc9PUnWZJct4wlhYYxtbCzblGkWg6td4GIqYxfQUC4jGQoas6ALVrWbwfNQGFhgXN0L\nU9UFRsCiatElbFmyNViW9J6kpzcPOURGxnj6jxMn88bNyMyz9417IyJz/9bSSmVmnBc3bgx5vvvt\n/e3m9imBQfoxzNut83FcnHPIDaTph3MYdyCNdB13lIL7d8MjT3ZMy0z1uPDAdFN916yvY5aVSns+\ng69jbiATN+xo/f5CF1V2es+JxWGPUnNJibTPZ5dvWam7LSAPpAFk/bM+Atbnvjhlpe4zyLe32rG/\ntB9nVs70pd8QSFgcEtFnieg0EV0hoseJ6N2B391DRI8R0QoR/TURXR1a+2EiukBE54no10O/u4aI\nvkxEFSJ6lIju8TkeySgL381xP8RJkkPYo6RlSnoOfUYpbFVWyk1rjFJWmlR5bpzjTtz99Tr/OyGd\nc5i0c+jKSrkBMVHLSjmisl/OYVJzDjn9rP0KpDGm+zO2l3NYC+0N4nQOB1FWGuy5ijOtdNOGjuGC\n5NN5VBtVVJv+4Qe9eq58ykqlgTT9KCv1LYWUhE9EGWUhCXvplVbqO0pBUg7MHYoOhAJpGK/HcCBN\nnME+khJuoFtUcpIow86hRAhxqgI2jfeQBtL0Ia10MmvFYSljY6ulziGw+fMO6FQvDNkoC2nP4f7S\nfgAY2bLS/x3AdcaYKQBvAfDviehOIpoF8GcAPgBgBsA/Afhjt4iI3tO5/a0AbgPwZiL6scC/+/nO\nmhkA/w7An3b+zW2Jo6zUrZO4axJXTuoS9DN8QuIc+mzOtnIXfJzDfs3ZiyutNEmXpte6uMtKB+Ec\nDiKQhiMqC4XhLivtV8pvlEAa95nnjvWuu7p/3y/ncJjLSrsCaWJOKw2XgvluRoqZIiqNCivsReoc\n9uo59BE1k7lJXKxeRNu0vW4PWNG71lxD27QjpZVKej7FM+UY5a+SUQrhx+cjznsdIzuQhllWyu45\n7FNZKUvkBZxD3yTKsHMYLLPcin4919JzGSWtNChknTg8Mn0EgA2kWWvxew7d/YXHWez0WdIvcegr\nsoFQWqnn5xawIaDd16gkKg6NMY8aY9wzSwAMgKMA3gbg68aYLxhj6gDuB3A7ER3r3PaHAfyWMea0\nMeY0gN8E8C4A6NzmTgD3G2NqxpgvAHgYwNt3Oh7JKAvfstLghjpKz6GPOEk6fEKalhlVZPiu7WdZ\naZyz4aQOrMTdkYhzty6pQJqoPYdR5hwmMcqiWJQF0kjKSpMMm+pnIE34dfnWt3b/21s5h3HNOXTv\nm2COQaxlpRQqK40xrTS4WeL0QAF2A7JSX0G1WRX18wGePYdbzNnzdQ5PLZ1CPp33ctYAu8nNpXOo\nNqqJlJVGTSttmzYMjKjn0Ne9Cm/ifTatm0qWBc4hx5ULn5MkA2m8e8mEQTbhQJpGi19WKh1l4Z1W\nGnYOI6SVBo/VCc6ZvO0vCH7OBZGWle70HtjK3WQH0jBGWbhh9lzn0HHPdV6FkzuSeM8hEX2EiCoA\nHgPwAoAvAbgFwNfcbYwxqwC+1fk5wr/v/L/73c0AnjbGVLb4/ZZIncOdRE2v/rq4SgYHvRGM2zkc\ndFppXD2H/Sy9TDKtNMlRFr7CF9hwDn2FAiB3DsOBNHGXlfZjBEycF4z6GUjT67EFj6GXc9hoyHoO\nfV7/RPyS7J7VHDE7h+KyUsMXooCNTa/Umc6hcF6bNJBmKjeFh848hOumr/M6Pkc5W8ZKfYWV+tqv\nQBpJoi2r5004Z4/bv9azB81zTMp6Px9zlMVAAmmEsxg5/WSSslJJwimwRVmpj+MrcA57CS9jTFdJ\n6sXViwCwfnEn/Lgc9Vbd64IFt39520AahuPI6TksjhfXP3+44vBv3/W3+LlX/BxrzVYkLg6NMT8J\noATgVQC+AKDe+X4xdNMlAOXO/4d/v9T5Wa/fhdduiXSURVxhI0kH0iQtTqL2rjm4zkmUst64nNtB\nB9LU68PnHEp61xzZrB1xUK3a8k0fwiMppKMsJIE01ao8kEYapBXnBaN+lGMDOwuvXs6hb7hM2Dnk\nlB9zxGGvzwTfK8fZdLZrhprv5kBSVhpORuVsRNbLSjnO4dhm50rSc+hbkrevuA+LtUUcmz22422D\nlDIlLNeXxWWlLLdFUlYaEF7cnjdu7yDQo6xUMufQ80JHcIYgx5WTlJVKRX14Fqa0rFQaSOMjDiUl\n3ID8goXEOexVVuqEoRODN83fhOzYRkN5+HE5duoddPfH7V+OUlYaPE5Oz2Fx3H62cvpSHa+55jXI\npj0a8D3ge5Z9wNhhI/+diH4IwI8DWAEwEbrZJIDlzv+Hfz/Z+Vmv34XXbuL+++8HAHz968Djj98N\n4O6et+t11V6SVioNdkgydXQYncN+zjmUlmxKHJBhdG5HxTkMv298N/BOeK2u2ll4PgTFQqvlF2ri\n1kUZZWGMdQ597q8fgTQubGrY5hxuVTK+3WPbyjn0TR7l9hwCm0NppIE0PhvdzFimqzw0zrLSoNvC\n2YgDgbJShnMo6YMKrwH8XZfjc8cBgNW3A2w8tkT6tITOoWRQfJQ+NO58xDEaw1p7ozfM90JHZiwT\n2ZXzDgSKUFYanPMpLSv1fV32cg59yrHDz5lo9qbnuex1gUSSVhp+Tf7My38G773rvduucevEzmEM\nPYfhCzGcnsNSpoRKvSIuK92JBx54AA888MCOtxuIOAzd/xEAX0enhxAAiKgI24v49c6PvgHgdgAP\ndr6/o/Mz97sjRFQMlJbeDuBzW92pE4cPPgjcccc2B9djY+abVirtOeSKykEMYe9Xz6E0kCauOYfS\nstJ+zDmUupuDSCtNapQFx5WbmACWl60j5ysO+xFIIykrbTTs4/JZ149AGt+wqSQdd05ZaZCtnENu\n8ihHHIZDaeIMpMmOZUUbz00Jir79XYHwD1FZKbfnMDRKQTLn0HdjnaIU7tx/J+46dNeOtw0SFIeS\ntFJWwqNwJEVQ1HOcK26wDNDD8RXMOfQVJ8HXMbesNNhzyH39u3Veqa+CWZjuGN18UFZaaUBU+gYl\nhcsvfc9llNmbYVEpSSvt9XoOPidbOYdxlahvJQ656zg9h8XMRlmpb2gRh7vvvht33333+vcf+tCH\net4usbJSIpononcSUZGIUkT0egDfD+CvAHwRwC1EdC8RZQHcB+AhY8yTneWfAfB+IjpIRIcAvB/A\nJwGgc5uHANxHRFkiehuAF8Gmn26LdJRFkmWlu2UIe7+dwzgCaaSpi5KewyiBNNLzP2qjLDjCq1y2\n4nB1lVdWGjWQhrPOheasrvqVlAJbO4dxXBzpl+Mufd8AfuIw7Bz6lpVKeg7duqhlpZySNVceyg6k\nCazjOl7istIGY5RFeF6bZ3miNJAGAL7ynq/gfd/2Pq/bOpw4lJxHgOFcRSjhkzi+kmAZty5yz6Hn\nhQ7JRQ53jOy0UsH4BaBHII3ne5SIkB/Po9qsssarBN1UF0azk4h15y0Y7iPtOZSeyx3f29Q7iXhb\nR65HQJXPOneMvcpKkxhlkVTPYT9J8p4NbAnp78KK0pMA3muM+QsAIKK3A/gIrOP3j7DC0S405veJ\n6DoAj3T+nY8aYz4a+Le/H8CnAVzu/LtvN8Zc3OmAJIE0PhvWJANpBuEc9nPOIee+fNcmmcQ6iJ5D\nyfPGLY1zSMpK+/XYOMKrXAYWF2255r59fmv6EUjjGywD2HOQzwOXLvHEYdRAmrhfW/2qeAD8ykrD\nzqFvWam057BfZaVxOofB+V+sjWAwGZJTVjpewuLaIlqmJS6P87kyHiWQRko5U45UVsrpVZQ6h+u9\nogznqlfPYaxppUwnyR1jvc3vuQ2KUUl5LuD/vDmB4sJTOK9FNx+01qr5z94M9GH69Buur+uIWHdu\nYk0r7eUcegTS9Cor3W5drzJzQNYH6+5PWlbKEZWcmbXFTDHWslJfErtnY8wFbNXcZ3//ZQA3bfP7\nXwDwC1v87lkA38E9JskoC9+yOmnJoEQc9hIncaVlRnGuJM5hrw2kj6hPKhAl6Z5PaTlq2HGJs6w0\nvCbO8kRHuQw8/7y93yQDaThlrIA9tosXozmHcb3+o3wmrAVGT8VdVlqtdv9M4hwOa1lpsOeQ6xxy\n00q7ete4ZaWZIi6sXmCNidjkHHqUlfYKrYir3MpRypSwXFv2doXc8+qeY05/l6RXMXhOOM5V+Fz6\nimzJCIxNcw6lzqHnYwte6OAImrBY9jlGIlo/l9yLKhLnMOhUcsShO5e5dM6/1FyaVipwYXu5gDtd\nENiurDSOC039cg5b7RYy437PmwukababSNPgxGHiaaXDhHSUhaSsVFIyKA12iNM5lIoTiXPYz0Ca\nONNKk06GDLtrvq5JeIPrG/8ffN5qtZ2DVCSCEtj8fEvKSrk9h42G7cnjCNFczt5Pu23Pqa/QA2xv\n5Llz/mt6XaDijlJIOuxIWg4PyJzDJHoOE3MOA6mj3J5DblppcHPMLisd74hDz35DQDbKotcGkjMG\nQMJ6WSkz7CUYiOLt0gicw67QFuYQ9n6klfpuxLnplcDmx8YpKw2ef5GgYVwgcX2H3Isqzjlca655\np0oGexx9wmh6rZO+Jlkl0kzncKsLPzuVlW4VSCMtUR+6stJOyX69VY/1IthOqDhk9hz6ppWGA2ni\n2pwlPdNMWlYqnXMoDaTpV1mp1DlMyrmNIg4lzqFPyqbkPPZaxy0r5fYcjo3ZkJZWy/88AkCpBKys\nbJwLT+MEADA/DzzzjP03fOhHIE3cF34k66Tv7V49h0mklSbpHAZHWXg7h8Ky0ihppedXz3snlQJ9\nHmXBTCDl4MThWnPN26UJh8R4uzSCEr5g6SWn9KyfaaVJ9Bxyy0qDCa6iEBVB8ii39K8wXhD1HLoL\nP412g11WCvCea0m4kqSMcquyUolz6BuSxL3QtGUgjce6KKMsVuorWKmvoJTx3CjEgIpDgXMYV8+h\n5Ap8KmU3p+027/4GUXqWlHOYZD9lKmWdp6jnP8pGnLsxBuTisFbb2fXqVVbqm9YrnXM4MQEsLfHL\nPJ2bxLmvoDjkuIaA7Yd85BFg/36/2ycZSBOln7gfI2CAnd/bWzmH3J5DzsWARJ3DwEgKVs+hoKw0\nKNa4Dsi6OGQ4hxKRETWQRoKbc1ipV1DMFL3WSMJ9ejmHXHeNI+qDz3d42LjvOoCxETcb98V5bJKy\n0qBTKem5BYTOIeM9Ctiy0tXGKmpN/57D/Hh+/TNBUlYKMD4TQs5cpDmHHqMsuO/tWJzDGEZZSEU2\nsNFzuFxfVnE4KLipl24Nd+PDKb1MKkF0FHoOtys92+55SzKQptdxSgNpJBtxX3ct6JIB/uIwLOol\nZaW+AraXyIgzrdQda73Ov6+VFb4QBaxz+PDD0cVhEs5hnCmn0kCafqWVxllW6i5yGLPxM98rx1Ln\nMDOWwVrTNn76bgTDQpQjuBZKC3jm8jM851AwyiIsKI0xsYvDyewklmpLqDQqKI7zxaFUnHjP2QsE\nlHDctV69ij79oj3LSn0CaUI9gL5jIqSjLLgXOsIBJZzXVWYsg0a7wTr/QHdZqa84zKVz6+9tViBN\nQEj5Cu3whQBxII3HhQdRWel2PYc7vCbDbjYQredwu/sLC1/OKItSpoRKo4KV+grKmbLXmjjY0+JQ\nMsrCt6xUuvFPqhy1n6EtcTqHEnehn65cUvMp222/8sQo4x6CG+sozqGk51ByHiVlpRLnsFbjpVdm\ns/bYlpb44lDiHPaqXkjKOY/rMyFKz2EvcRhnzyG3rJSox98AT+dE2nOYH893iUPf+5KMDQCAwxOH\ncWr5FK/ncGzzYO4dew57bFZ9RY2UqdwUrqxdETuHvudfmlbqhAnnvoDuDSsn1KdXWiknkIZ7kaPr\nsXm+JoNBKhznVlL6CnRKe1t11vkHugNpsmN+PYe5dA7Vpk3h4vTVNWq6AAAgAElEQVShBYUUJ0E3\n+FzH6hwKykqlTt5Wa5MaZcG50OECsZZryyhnVRwOhLjKSqOIvH70/MQ5SkE6C62fcw4lPYdxlcf1\nWicJ5EiiL29Q4lB6Hrlppc455KaHVqs8UUlkS0svXOCXlc7PA5WKvzjcKpAmjp7bflYTxJlWKh1l\nkZRzCPQWh3E6h/l0fn0D6bsRkc5UBKw4DH71Iegctk0bxhiv0ApuUmZUpnJTWKwtip1DTviHJK3U\nCROAt+kMurAcl0ySVtqVhMsMVpK8/sNlpYkF0ghHWXCcw3w6L3IOpWWlXJcYkDmHW5WVbrduq7JS\n79dku3tDmVRZKef9tq+4D2crZ7FUX1LncFBIR1lI0kolpZe+oqYf8xGTcBeibrAccQVySINU+lHW\nm0TppVQcBo8zTnEYxRWdnbUjIlZWeGWlxaIVa1zHsVwGzp+XlZUCw1lWmmSCsVQcbuUccnsOOf2i\nwXUu2ZZ9gVDQc8jZUDhHgrMuPFORs8mdK8wBAG6eu9l7jTQggzsbMSpTuSlcrl5mO4fcPsBe4oSb\nVsoSeQEXlpP4Kkkr7UrCZTqHorLS8P1JQ1QEgTQcx90F0nDmHAbLShstRiBNR0i5UmzfcyIJLZI4\nh30vK01wlAV3Hed9WhgvoDhexIkrJ7TncFBIeg59NgbhDXWUnkOf6hmJGO1nbL2v8OWKw/Ca4No4\nAjmkZaX96Dn0dQBTKfvvuwAcjnMocU765RxKhC/nsRUKwMwMcOIET7AVi9Zt5IrDUkkmDu+5B3jx\ni4HbbvO7ffh14iNOkhR5ve5POgIGkDmHvmWlwdf/2hpv5El4HuZOn8uRnMP2xubYd+NZGC9gtbFq\nj9FzIxKcqcjd5LrHcmjikPea4FV/n6RSYPNmNe4xFgAwmZvEuco5EBErGTJqIA1nCLukHDjcc+ib\n+Cp5DsI9h9xyTXeMLOeQWVa6SRwy+gf7MsqCUVYq6jnsCKm2aYNA3q60e41wRWU/0kp3eu76GUjT\nNnYTtd3j27bnkFGOyu2TPjxxGI9feHygZaXxfsoOOXGWlUrLGiVjCpJ0DqNsIPs1yiJO5zBJ50Ra\nQunWplLRnEOfzXGvURY+aaUS4SsV545rrgFeeMG6er4UCjLnsFTizSt0HDwI/NM/+d9+q4sI24mT\npMfUSErN+9lz6FtWGnQAq1XerElur+KmKhCGOFwPieGWlTaqaJs2a6aipITP8Vc/9Fd45dWv9L59\nUEBJy9w4zomUqdwUTi2f8i4pBXoINkFZo+9GPNiXxxU03PMPbO77jLvnUDTKIjyWxef8d861e79w\nxKg7Tu6m36WVcgNpqo2NnkNuWal0FqY7Lz6fJeH+2djSSrdxDrni0LcUNemyUsBedHvk3CNaVjoo\ndhJfSZeVhsVolF6tpAJpOD2Hw+4cSstKJeckihAK3h+351CyyeU6h9LXca9RFr7CFwCeesp+nZz0\nXxOlrPTCBb5zyKWXOOSm/CbRcyj5/Omnc8jtOeQ835L3jdQ57BJszECaarO6vqH2CWzp6jlkRvID\nwD1H7vHe4ALdzpVPUqk7xuBmkLM5ljKVm8JqY9W7pBSQiZpeg8O9BPMYP4US6HYAOb2bvTbVOz0H\nQbHATgEVjOkYS20Ibd+yUnec64PiBWWl7FEWnd5gziiLXDqHeqsOY4wNpPF0fJ3Lxjn/0r7UXmIo\nlrTSrXoOBXMOpaWoPscpLc91HCrbioxBlpWqcxhDWWk/A2l8ncOoUfJxl5X2cg59ynN7iUOftMZ+\nuaJx9hy227ZM0I2Y8BV5wfsbhbTSJMJ2AOBTn7LOJodCYaOslOMCSstKufTq1ZVULsTdT8x93wyi\n59CFDwG8nkNJOba051AaEuOcQ84mRDIbMQrhaH2fstKg6AKSEYduQ8YRvl0pm0bWc+gr9KR9eRLn\nFujexPsKlK4eQGkgjeccRneM3LJSYOOcZJGVBdIwR1kUxgvsQBpX3lxr1dBoM3oOA32RnPPB7QsG\nZP2bvVxAH9HVMi0YY7ougHmNsqDuACiJoAT8ZoRK3PYgty3YvhNNKx0QPg6UpKxU2nMoLcdLcpSF\nVFT2Gm/gW1YanBcGxDvnUFLqKSnrJdooCwX8N7jh4xzWtNLg88a5yCEttQWAN7wBuPde/9sDcufQ\niUNuWSkXacpvkmmlvUaQSEvGpWmlPq/lYtEGFgG85zub3bjPuMtKw7MHOT2H1SZPHEpLWKUEN4O+\nztVYasxuxjoiiuOcSHHPUy/HYCskzqE07j6Y+ioVeZxgn2D5X8u0QEReYSNdTt4QlpW64wyOe+CK\n2EijLNJ+PYfARt+hpKyU25cqvYAQ7t+UppVud59EtEnk+R5rLwfcZ17nVomq21VnRC0rfdMNbwIA\nLSsdFEmmlUqckyjOYVJppXH2HKZS3QLK4eP4Jl1WGtVN5ZaVSpzDpAJpiLpfX9KLHNyyUgkukIYT\nUAIAc3PAyZPDW1baj2AZ6WeCz4UOF6zU68JPXM5hqbQhDjnPdz6/4TjGXVYa3BxXG1UUxv2id/Pj\nG84hZ2PcNm202i1RWSmXrkAaz7JSoNuV4zgnUXl+6Xnv20qCVMJlpZwgIUkgjbTnMLjR9e357Oo5\n5JRrRgjbkZaVBnsjfc+JE2vclN9SpoSl2hLWWv7OIbDx/mYF0gjczbC7H6dzGC69dPcpcRy541Xc\nffms6eVucstRueLw6MxRfPGdX9S00kEhKSuVBtJIxMmoOIdx9Rz2WgckV1YqHSUi2YxLy0rjdg5d\n+atLR/URh0D38yYtz+WWlUqQBtIcOQI8+eRwlpUGLwIA8vMvnbPq8xnpLiBwL/xE6TkMikNOGXEu\nF00culQ8nz7AbDq7Xla62lj1F4dpG3TB2YS4crV6q84uj5MgLVkLCq8kykoB4Ka5m9afNx8k4yWk\ngTTBnkPO89bVc8gcZeHW+Tq3YdHFcQ67nDzPdeEyVmlIj+/9FTPF9fcbxzncV9yH86vnWT2HQLdz\n6N1zKCwrFZceB4Sel3MoKCsFevcdipzD9s6JyW6NCVzFlNyXpGz/e49/r9ffjLjYs+LQmJ03n73K\nSn17DpMMpBmkcxil51AqDrllpVEi+ZNKeZQG0nB7DrnBGkTdz4Fvr5ZEHEZJcJUiLSs9csR+nZ2N\n57gcwecMkAU5SUeJcCoeuM6hWxe++MZ1Dt1oD5/jLJXscw3wnUPXyyrpOWybtqisbrWxinza7yDX\nA2mYToYTo9xNroTwvDbfssbgOUlKHP7qa38V77zlnd63l/QBSgNpNjmHvn1yIXHOCTZZdw49ndvw\naA/uKAs3RoFTVhoMwJGE9EhGx3AvqiwUF3B25SxrlAWw8d6pNWve5aiSRNVw6XGsPYc9RJ7P+dxK\nVErcvJ3eAylKgUBdF4qkQjTui2/9Zs+KQydqttv8RykrDW/OJD2H0l4tyZy9YZxz2Ov+pHPepGW9\ncYnz8HFyRF7w9RW3cwh0P3dJOodJlJUWCsDion2+OPflxOF3fEc8x+UIPmeAn/AKC0rpKJEoFQ9x\nicOwc+juy+cCq9Q5lJSVBj/L26btfQXY9Rw22020TMtbCGXHsmi0Gqg1a6xNSDDuPu6y0qA4lCZY\nckRlFO696V780Tv+yPv2knLIXs4ht+eQczFAGkAU3OjWW3V2kBDHOUxRav3+uOWhkrLSLhHLOM5C\nurDhHDLeN/uK+3Cucg7L9WVWGq5771QaFe9Sw2BaKTdoB2BeeAj1D/qIvK36+RJzDj37nsPhMtJk\nVBWHI4KkPNR3Xa9gGclmiVNWyt3U9ausNM6eQ6B3kE1cc96kaaX9cFM5Ii8svOJMKw3fn684DD82\niXOVRFlpsWhHUnCDZZw4fNnL+n9MQcLiMGnnMK6y0l7rAL5zyAly6kfPoa/D3PX6Z2wMipkiKo3K\ner+hr6gkIuTH81ipr7A2q06MJlFW6hJVAV7PYVB4JeUccpHMHuzVc8hNK+WIk7BzK3GTfM9/+Bg5\nry33fHOEr7SsNBxI47uuMF5ApVFhlb4CVhyerZzF2ZWzWCgueK9zIzBW6ive8zfdRQTphYDIzmFc\nZaW9eg5jGmWx1TpuryLHqR8WVBxuQ7/KSqXBJnE6h0kPyu5Xz6FvIEc/gjWS7NXilFAG0xO5onLU\nnMOkxCG3d7BQsC627zmU0ss55IrDJJxDaVlpuOdQ6hz6kMvZc9lsyp1Djjhcf/0zhFc5U8ZybZnV\nb7h+nOk8Lq9d9i5FBTbmKiZRVupKXwGBc8gUJ0kjKfUMOyDeaaVjstCQLnHIKHMLOie+gTSbRlIw\nLlhIkkCDYSNSx4sjtF3PIdfJnshOoN6q48SVE1go+YtD99yt1Fe8ncNgVYBklAVXZG9KKxWUlfo6\nh8H7apu2V+m+ZJQFICtHDfcqqnM4QvgEO7gNVTvQly5JK417zluSgTT9KkeVRsLHPedNmjIbtVcr\nijgcRudQIg5TKSu4XO8357FJmZgAzpyJP1hGSi/n0KdyodHoHiUi7fmM0zmUlpUGH5vP+XAQbfQd\nJu0c+m5Wy9kyVuorMnE4nseF1QusdW5+WhJlpV3OIWNTPYhAGi7BY2SJvJC74BVI03GE3IgPbnlu\n27Sx1lxDftzvDbCprNTDAZHO6wQ2+mA55aFd6ajMctRguI93WWmn57Da9E8UBqzDP1eYQ6VRwVxh\nzntdLp1DtVFliUN3EYEVSNOHRFvAzzmUlpX2Cr9Jp9I7VlkES48Bv1EWvY7T5xhTlEKKUl0XLFQc\njgg+V+ABWT9Nr55D383SsAfS9GvOoXSYtM/z1q/URek6ybnklIcGxaE0kMZX5AHJicNwgmUSzuHV\nVwOPPz5a4pA7AibKSJxhC6RxAUnunPh+jjtcaSkngCiYVuq7LnhRi3sFfnxsHBerF1kOIGA3rOcr\n51m9TK4PLYmy0nDPIWeUhRNeSY6y4JBJ8Us9g72UgL/DTETrG1bOhQciQjadxVpzDdVG1fv1FU45\njds5dIPiWYE0qY3+Ta5TJgnOceKQExrlmM3bFDPO+20yN4nF2iIqjYr3+9sJdO5IkL6MsvBxDrco\nK+WWo/oep7QPULpOkmA8TOxpcSgVbFznytcBlAbSSK7497P0UhK2Iy0r9XUOJeWh/VonOZec8lBp\nWWlQaPimjgLdTpRUHPoKWKlglnLttXbDX+CZNIkhKSsFus9/nBUIbl1SPYdA9znhusvFohWHnNd/\nMK10dVXoHDI2x+VMGWdXzrKdw4nsBE6vnPbuSQI6G8hmcmml9VYdbdNmlTUGN1mcKP8kCR6jr8gL\n9lIC/FJbbskgsCHQ15r+c/aCblLcgTRAIAmU0c8nLSvt6jlkOoeVRkXk8F81eRXr9gAwX5jH+cp5\nlnMoEdnScTOinsMIZaXBddLyUJ9RFj3X+QbZhEKqVByOCL6bCkm4TC+RF2dZ6aCdQ85jC5aDxdVz\nGCVYph9ppZKQHmlZKdc5dOs4zqG7v7U1+//cHlOOgJWG9EiZn7dfjx6N936kSMpKAZk479dnQpzO\nISAvqwasc7i4yOsXDZeV+lxIkPYcAra09MzKGfamczI7iReWX+A5h52ewyTKSoPOlW8YBDA6gTTs\nnsNeZaWcVMk2L4kS2BCH1SbPOQyKw7idQzezk/O+6SorZdxf8LFxhHZx3PYcuuAoDvdcdw/r9kBH\nHK7yxKELseHOwnS9cnE7h73KSn2eg17vG2l5qHid5wUSidAeFlQc7oBEsPUzkCaunrekew7DJYPS\nURaSnsO4A1GkPYdJB9LkchsOSK3m75y4UrzVVX+HTVJW6tZJzokU16Zw/Hi89yNFUlYKJO8cJhVI\nA8h7ZwFgchJ44QVeGbGk5zD4OuZu4CeyEzhXOccXh7lJnF45Les5ZLo7UlzvFCctcxQCaYI9h5Ky\nM4AnTtadQ+aFh3Vx2Kiyeg7d+fcNpAmOzZA6h03DK4d0ziHnnEgDadbLSpur3ufR8b6Xvw+Vf1th\nrZkvWuewUq94VwZInMNgr9zQppWGnENpeajvZ1CUstJRdg5H62j7SJJlpZzyUGlaaVLOoXQD6dY6\ncRenc9jP8txhnHModQ4LBSvwgA0X0AeJOJS6okF3M4myUgD4r/8V+LZvi/9+JEjmHAL9SYvdjc7h\n/v3AI49sOMY+RA2k4Zb+lTNlnK2cZW86J7OTeOTcIzhQOuC9xo2yqLVqrKHcUvLp/IZz6DvKIiC8\nfMVJ0nSNsvANpOlRVsqZz8cNGwG6y0p9ncNgX55vWa/bULdNm32M6+KQ2XPYJfI4ZaURAmkyYxn2\nRRwiYq8ROYfjeaw2V9kXp5xglorDtmmDQDsGxIjLSiP0HG5yHGMaZeGOc5TFoTqHO9CrrNTHlZNE\nyUvXSUZZ9GummW8JJdB9LuPsOQwGVgC885+kYA6eS27PoXMAOeuC4pBTVlouA8vL0ZxDSaJqEmWl\nAPBd32Uf4zASFof1uv3ZTkjOv7SfuJ+BND5ro/QcHjgAfOUrViT6kuScQ8CWlZ6t8HsO18tKGT2H\nXQElTDEqwY2z4PYcBstKh7XnkBtIM5YaAxGtB6lIeg65/azBslLfnsPg5tg3kIaI1p1RUSBNs8oK\nzRmj7kAajsiWlKMWM0VU6hVRWamEucKcrKy0UbUlxIz3thPMbHFoOufRMy02SlppPxzAKKMsNJBm\nF+PbuyMZwSDtOeyX4zWMcw7dWrdhlY6y8HUO+5H6Kg3WGNaew7A49C0rLZejlZVyha/b+CdRVjrs\n9BKH3EAg7kUm1xfM7Sd2xB1IE3z9c0ZZAFYUfvWrViT6Ekwr9X0PBD/rxIE0aUFZ6fJpVs/h+kac\n0YMWBbdhZZeVBgJphtE5DLqA3LLGYCBKUoE0nIsB4Z5P317RYOmrpKyUI2rCgTQcUSMNpJGmlUpw\nZaUr9RXv97d7b3NDc5wzxz6PrQ3nXNJzC8jLSn1ek8H5oIB8lIWvqBz1stI9Kw45oywkQ9ij9hwa\nI3Og3MZuB0e/r7MA43QOw+efM+fNESUQyOcYezm3Sc455DiHbpPLLSuN6hxKElWTKisdZtxr0s1a\njdM5JLLvZXdfnM+E8GdkUj2H3FEWBw4AJ0/ynUPn1ItGWXADaTJlnFo+xRJ5ADCVm4KBYW0EC+nC\nerBGEs5hLp1DtVlFrVVjpWXuxkAaYKP0zBjDSud0Ik8aSMMpKw2OIOGc/6CAlQTSsJzDVGjOISPY\nRzLXbzI7ictrl0VppRKumrgKzy09Z8WhZ2VAfjy/LmA51QRRy0p9ncOky0qDF2I466RBNioOR5Q4\n00r70XPo3KedRJ5b5zZZcY+kiFJWGnYOub1TgN/5jyIO+1EeN6xzDgdZVirpOUyqrHSYIeqeTykR\nh5zzGHxN+r6Og8fn7i/unkMnDut1vnMY/OpD8H0jLSvlbOAPTRzCNy9+E0eneRG6k9lJAGBtBLtc\nmiScw3Hbc8hxXIL9fEM75zA4yoIZLOMG06cotWOflqM4XkSlUZEH0jBcOVcKDPB6PhN3DgVzDjc5\nXp4idqG0gHOVc4mJw3K2jHKmjMncJM85bPCdQ0lZ6abyXI/ne6uyUu4IDImT59bF2nOYko0FGRZG\n62j7iHQD45uWKe05lIiFsMjw2dA5h8AYuwGNIqB8r9x3Jfh5rpME0oRL8aTBPr4CVtLzCXSfS27p\npdQ5XF21x+dbngjIy0olDqCWlW7GvZ7d8y5xDjml39wLTdLQHKk4DI9k8S2PBoDbb7dfDx/2X5PP\n2/tptXjicP38M0v/bt13KwDg+BwvQncy1xGHDMexmCkm6hyu90FxyhrHup3DiexEnIcoIhw+4d3z\nluKX8AHdoS0csewCgTg9h+45A3g9n1LncF0cMnsOJamjTtQYY9A2bZZzWxgv4NTyqUTeNwBw/cz1\nLJHnRllIykoTcQ57lJW22i2kx2NyDvs4AmMvlJWO1tH2EWlaadxlpdLZcEGR4SO6gqMl3Po4+4vc\nWm7PoUSc9xKUcT62friwSfYcOvfJ8yI1SiVbiscRh0GRx3ktO6FhjIpDR1B8SZ1DyYWmuMVhlJ5D\nd38cBxwA9u2zazivq1TKvu45F0g2OYeMzfGtC1Yc3jh3o/9Bwm4eAbBSR91GfK3lPxQ9Cs6F4jiV\nmVRmNMpK2/yew65gGcm4h3aTJU66eg49z3+Xc8hwbpN0DsdSY12jLFhppe2Gd8JmkP2l/Xj8wuOJ\nOIcAcMPsDZjLz3nfPlhWynYOBT2HXOdQXFbaq+fQQ+RtchylQTae64LikNNjPSyM1tH2kTjLSqXB\nMtIZb+HUS+5G0IkbyYaOs/HvR1ppnIE0Ule0V7iPxCnmiMPl5Y1j5DqH3A21xDkMC1huWWm7vdED\nt9cJi0NuII3UOfS9yOFcYleFkETPYdA55LyW3Xou7j2QRM/h0emjePed78bhCYa9CeC2hdsA2ARS\nXwrjBVyqXmKJhSgE+8lm8jNea0YhkCacTMjpOXSJnpzXiEvLlAbSrDXX/MtKQ84hVxxKXNEra1dY\nr0lpWanrr+OeRyB5cfj+l7+f10/cKSut1Cs85zDFH5MSFFC+F8NciJAxZl2Ux9pzmLBzGAzAUedw\nhIiSVsrtOZQ4V1yRF3QOOU4et6yxl0sWp3MoHWURFoc+G0LJc+3WRS0rbbX8y3OzWeDChY11XOdw\nbY1XiucCaSoVf3EYDpbhOofqGm4Q1TmUlqj7XuQYG7Ov96CYlIRbAfE7h1Jc320SPYdjqTF87C0f\nEx1n65daSJH/FRWJSxOFiewElmvLrPsblTmHkhIyF7bDdZddkJA0kIbj3DoHCkimrNTdH8s5FJaV\nBvvrOMcIWHEIIDFx6CoKfHEXYlYbq5gtzHqv60tZqcdrkojWnzfX++c1yoJkTp60V1E6H9G9/l3Y\n1KiJwz17XX5Yy0olTlIUUcm9P+m4B6D7ajpnk8tNEA0HZAzrKAup45vLRes5lDiH3EAaaemr2/hr\nUukGQXEo6TmUlqhzkojdMXIvTkl7Dl16KLfnUIp7Dywv24slOxGl5zAKHGEIyPq7ojCRncBibZF1\nf8FAmnp7eOcc1lr2A48dSCPsOaw0Klhr8sqBc+nces8nq+cwQiANV9AXxgtYbfJek8FRFtyy0ma7\nyS59BYA3H3szAAzl6xHoHmXBTSvlOr7hslJOkA3XBQyPpOCKNUej5RdI06uMlVNW6spsOSXLw8Ce\nFoeSOXuSQBqpOEzSOZQkqkruz52XtTV+sAPg7xxKAmmkZaVSNzXKKAu3OU7COZyZAS5elItDbs9h\nraZJpUEG5Rxykogl4nCrnsOdHp90JEsUguKwXN759l3hWwJXIimSdg4ns5NYqi1htbnqfX+ZsQxq\nTfthklQ6JJdg6SU3EKXRarBLj9eDhJjP21RuCpeql1hlpa48EZDNOaw2q8iN+f/BmcxO2rJSZs+h\nSJwEykq579EfuPUHYO4zQ7vpz4/b12SlwSsrdYnCcQfShNcBjJ7DPpSV+vbPSstYpRd+hoU9LQ6l\nYsin5006yqIfgTSSzZm0rFTqHPpu6vrRc8h5bFwhCsj7MKP0HCbpHC4sAGfP8spKo/QcallpN1F7\nDqWfJRxR2S/n0Ef8BsVhUmWlpZLtOfQVh13vbebGP0mSdg4nc5NYXOM5h7l0bt2V4/ZPJYUTawDv\n+e4qKxWEtnDF8sHyQZxeOY3l+jJKGQ8LHN2BNNyy0lqrxnY39xX34fml55GilPd5HKOx9Z5Ddllp\nuyFyDoeddCoNIsJibZGdcupKluMMpAF6uHJm5+eul3MoKSv17Z/tWY5Kfvcn6bkdFlQc7kCvstK4\neg6lIRLS8kSJcxWlrDR4Ln3LwfrVc+jz2IJBF0C0tFKuqIwiDrnOoVQcnjlj/9/3GCU9h1pWupl+\nOIdxf5ZIxWE4kMbn8eXzsnmdUXDO4coKXxxyN/5JMoiew8XaIuv+3DECQKVRYY3qSIrgMXKeb+cu\ncEuPC+MFVOoVtqg/UDqA0yuncXbl7HrP3E4EXdFqs+otNJxzKBGHJ6+cZD0uSc8bsNFfN4o9YT7M\nFeZw8spJfpBNs5qIcxh25XwurISdQ9/ewfB9eYvDCM6hisMRRJpWKgmk8d2cSUvB+uEccsRhq2WT\nCbn3F3YOfcWhxLmViEMnuoKPTZJWKnFhuX15krJS16tYqfDKSotFW1746KPAVVf5rQkKbYlzqGWl\nG0TtOYzyWSJ53pJwDgchDpeW7P369BwePQpcf739/2HeHCTuHHbKSjn31yUO6xVW/1RSuB5AQNBz\nxUyGBIDiuKys9ED5AJ65/AzWmmuYzE56rcmMZdb78ip1f3EeFIecY5wvzuNi9aJoRAfAO/9RAmlG\ngcMTh/HkpSfZ4lDiHDoBxXEORWWlvXoOBaMspM4hp8dR8t4eFvasOJSmlfoG0kQRJ+5+knAOuT2O\nqdRGMiH3ON15MYZXVhrsH/R53sJz1zg9ny6KH0jWOZSUh3LXpVLA3Bzw/PP8DfXCAvDQQ/7iUOpu\nOlHpWz65FwgKbemcQ8lnyaiUlSYVSHPmjHUtffow3/EO4Ed/1P7/MLsSifcc5iZ3tXPohqn7BgN1\nzTlkDoqvNCrsstIDpQN44uIT2F/a790rR0TrpaWVhr84lzqHTrQS/Hv5Njm3vs6V6znchWWlAHCo\nfAhrzTXWe8alxXIccLFzKAh7cc8ZZw2wOZCGJQ4lPY5aVjqacIawc8saezmHPhsmacKj1DmM6hJw\nj9O5gI2GvS/fUs/wgG2Jc8hx5dxzEHfPobT0r1Sy7p9bxym/3L8fePpp/95Bx/y8/Xr4sN/to/Yc\nckNzdjPBcymdc8h5jwZFZZzisFcgzTCXlb7wgl9JaZhhdiWcyEjSOeT2HI6KcxjcUPsKL7fx5Pal\nSh3ffcV9AKxI5+BKSzni3K3hikN37pwT64MrhQT4aaWurHdY36NROFA6AMD2mvpSSNsAIu5IFlHP\noaBkMzxawnuURZSy0ghppSoORwxfcdhLnPj0vEnEQtLOYZ+JA1IAACAASURBVNI9b84F5FztD/au\nAbKeQ865DD82blppu22dUR93QSqgikUrDo3hjRsArDh87DGbQMrhxhvtV87zJnls7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A3wXwbmPMlwd5fIrSL7b7GwzgiwD+b2PM/sDtzwF4szHmHwdxvLuZHn+DrwHwDIDx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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9976338470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
    "plt.xlabel('timestamp', fontsize=12)\n",
    "plt.ylabel('load', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our data preparation for the training set will involve the following steps:\n",
    "\n",
    "1. Filter the original dataset to include only that time period reserved for the training set\n",
    "2. Scale the time series such that the values fall within the interval (0, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create training set containing only the model features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (720, 1)\n",
      "Test data shape:  (744, 1)\n"
     ]
    }
   ],
   "source": [
    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
    "\n",
    "print('Training data shape: ', train.shape)\n",
    "print('Test data shape: ', test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scale data to be in range (0, 1). This transformation should be calibrated on the training set only. This is to prevent information from the validation or test sets leaking into the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01 00:00:00</th>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 01:00:00</th>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 02:00:00</th>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 03:00:00</th>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 04:00:00</th>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 05:00:00</th>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 06:00:00</th>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 07:00:00</th>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 08:00:00</th>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 09:00:00</th>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     load\n",
       "2014-11-01 00:00:00  0.11\n",
       "2014-11-01 01:00:00  0.07\n",
       "2014-11-01 02:00:00  0.05\n",
       "2014-11-01 03:00:00  0.05\n",
       "2014-11-01 04:00:00  0.06\n",
       "2014-11-01 05:00:00  0.12\n",
       "2014-11-01 06:00:00  0.22\n",
       "2014-11-01 07:00:00  0.35\n",
       "2014-11-01 08:00:00  0.46\n",
       "2014-11-01 09:00:00  0.55"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = MinMaxScaler()\n",
    "train['load'] = scaler.fit_transform(train)\n",
    "train.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Original vs scaled data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NzKyvMk8sEfFkyepiSbOB04FtwOiyzVuArelyeX1LWlZRW1tbx3Jrayutra29jtnMrD8q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\nj7xI7rYpfq+Vlvt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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f997639dc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9976313cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy[energy.index < train_start_dt][['load']].rename(columns={'load':'original load'}).plot.hist(bins=100, fontsize=12)\n",
    "train.rename(columns={'load':'scaled load'}).plot.hist(bins=100, fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also scale the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-12-01 00:00:00</th>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 01:00:00</th>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 02:00:00</th>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 03:00:00</th>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 04:00:00</th>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     load\n",
       "2014-12-01 00:00:00  0.15\n",
       "2014-12-01 01:00:00  0.11\n",
       "2014-12-01 02:00:00  0.10\n",
       "2014-12-01 03:00:00  0.10\n",
       "2014-12-01 04:00:00  0.15"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['load'] = scaler.transform(test)\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implement ARIMA method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An ARIMA, which stands for **A**uto**R**egressive **I**ntegrated **M**oving **A**verage, model can be created using the statsmodels library. In the next section, we perform the following steps:\n",
    "1. Define the model by calling SARIMAX() and passing in the model parameters: p, d, and q parameters, and P, D, and Q parameters.\n",
    "2. The model is prepared on the training data by calling the fit() function.\n",
    "3. Predictions can be made by calling the forecast() function and specifying the number of steps (horizon) which to forecast\n",
    "\n",
    "In an ARIMA model there are 3 parameters that are used to help model the major aspects of a times series: seasonality, trend, and noise. These parameters are:\n",
    "- **p** is the parameter associated with the auto-regressive aspect of the model, which incorporates past values. \n",
    "- **d** is the parameter associated with the integrated part of the model, which effects the amount of differencing to apply to a time series. \n",
    "- **q** is the parameter associated with the moving average part of the model.\n",
    "\n",
    "If our model has a seasonal component, we use a seasonal ARIMA model (SARIMA). In that case we have another set of parameters: P, D, and Q which describe the same associations as p,d, and q, but correspond with the seasonal components of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Specify the number of steps to forecast ahead\n",
    "HORIZON = 3 if demo else 24"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s look at an autocorrelation plot of the time series. The example below plots the autocorrelation for 48 lags in the time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oicnMzgbOAdoAC2v86t/A7e4+POpAC1UOBQJg6FD48MMw5lqC+fND+/C8edo1\nTqSUGrQWk7vf6O7bA+e7+/Y1HvsksTiUk/POCyMzXnkl+muX6xoxw4fDgAGlKQ7lmqNSUo5yS0t+\n8pkH8ZWZnbjuQXcfXYR4UqFFi7A2zWmnweTJ9RuqV4mWLg13U2+8EXckIlJTPsNcb67xtAnQHZjs\n7scUM7CGKJcmJggjdnr1gp/8BC5O+dKHw4eHYa1jx8YdiUj6FDzMdZ2LbUrYMKhHFMFFqZwKBMDc\nuWH4XZo7rNesgV12CXcQXbrEHY1I+hS6H8S6lgE7FBaSQJgxPGQI/Pa30S2DXG5to48/HjaY6dy5\ndK9ZbjmKg3KUW1ryk888iEdZu0HQesBuwIPFDKoQw8ps+c/VqxsxbdqpHHPMy+y999sFX2/u3Lm8\n8MILEURWGqNGnUDHjlO54orpJXvNcstRHJSj3NKSn3z6ILrWeLqKUCT6uvvAYgbWEOXWxFRt4sSw\nYNmMGaVbwTIJpk0L+wLMnauOepG4FNTE5O4vEOY+9CYs+T0MmBlphCn34x/D0UfDRRfFHUlpXX89\nDByo4iCSVLUWCDPb2cyGmtks4GbgQ8Idx8GaBxG9P/whrIn/0kuFXadc2kZnzQr9D3FsKVouOYqT\ncpRbWvKT6w5iFnAI0Nvdu7j7zcDq0oSVPi1awA03wOmnl/9G7/m4+GK48EJo2TLuSESkNrmW2jgK\n6Ad0Bp4CHgDuyMyuTqRy7YOo5g5HHAEHHBBWNK1UL78M/fvD7NnQpEnc0YikW6H7QWwM9CFsNXoI\nMBp42N3HRx1oocq9QEBYk+iHPwybvO+zT9zRRM89TA789a/D0hoiEq9CO6mXuft97n4EsA1hu9Eh\nEccoGW3bwo03wjHHhP0j6ivpbaPjxsG//w0nnBBfDEnPURIoR7mlJT/1mijn7l+6+wjtBVFcv/hF\n2If3lFOim0CXBKtWhZFaV18d3cYtIlI89V5qI8kqoYmp2ooVYemJX/4Szjkn7miicfvtcN998Nxz\n2uZRJCkK2lGu3GSbSd21a9es2wNWVVVlnQ2ZlPM7d96USy89hQ02mM3AgfvGHk8h5++/fzcuvxwe\nfnhtcSin+HW+zq/087PRHUTCPfpomEz25puwxRZ1n5/UvXL/+EeYOhUeTMAiLUnNUZIoR7lVUn5S\ndQdRaY44IgwLPf74MJGuHNvuP/sMrrsOXnst7khEpD50B1EGVq2C7t3D47LL4o6m/s45J/w3DNf8\ne5HEiXTD57HrAAAKE0lEQVQ/iCSr1AIB8MknsN9+MGpUGOFULj74IOx58c47sNVWcUcjIuuKej8I\niUHr1nDvvXDiifDhh7Wfl7Tx2b//PQwalKzikLQcJZFylFta8qMCUUYOPjhsMHTYYfDpp3FHU7fH\nHoMXX4TBg+OOREQaQk1MZejSS+HJJ8N8ghYt4o4mu/fegwMPhH/+M6wtJSLJpD6ICuMOZ50F06fD\nU09B06ZxR/Rdy5eHonDaaWGIrogkl/ogKowZ3HQTbLcdHHssrFy59ndxt426hyXL99kn7LWdRHHn\nqBwoR7mlJT+xFQgza2lm483sXTN72syyNpaY2Twzm2ZmU8zs9VLHmVSNGsFdd4U/TzwRVidkp46/\n/AXeegv++lctpyFS7mJrYjKza4DP3f1/zWwI0NLdv7fpppl9AOzn7l/mcc1UNDHV9M03YV/nnXeG\nW2+N90P5lVfC3tqvvgrt28cXh4jkL6lNTH2AUZmfRwFH1XKeoaawWjVpEjqCJ0+G3/0uvjgWL4a+\nfcNdjYqDSGWI84N3S3dfDODui4AtaznPgWfMbJKZnVqy6MpI8+ZhVNO4cXDCCVWsWVPa11+1KhSH\nk0+G3r1L+9oNkZb240IoR7mlJT9FXYvJzJ4Bak6RMsIH/qVZTq+tbaizu39iZlsQCsVMd3+pttcc\nMGAA7dq1A2DTTTelQ4cO/11Uq/oftRKfb745XHllFRdcMJXDD+/G3/4GM2aU5vUff7wbTZtC165V\nVFUlIx+5nldLSjx6ruelfF7987x586hLnH0QM4Fu7r7YzH4APO/uu9Xxd4YCX7v7dbX8PnV9EOta\ntSrMXr73Xrj/fujcuXivtXp12PznjjvgjTdg882L91oiUhxJ7YMYBwzI/HwS8M91TzCzjcysWebn\njYHDgLdLFWA5Wn99uOqq0GH985/Dn/9cnF3p5s2Dbt3g2WfDbGkVB5HKE2eBuAY41MzeBboDVwOY\nWWszeyxzzlbAS2Y2BXgNeNTdx8cSbZmovo3s1Qtefz3sv/Czn8GXdY4By4873HNPWIDvyCNhwgTY\ndttorl0q6zY1yfcpR7mlJT+x7Qfh7l8A/5Pl+CdA78zPc4EOJQ6tYrRtC//6F1xwQVgJdsyY8MHe\nUEuWwBlnwLRpMH48dOwYXawikjxaaiMlxo6FM8+E7bcPmw8dd1x+O9RVq6qCk04Kdw3XXAMbbVS0\nUEWkhLQWkwBhSY4JE0IT0eOPQ5cuoVgceeR3P/BXrIA5c2DmzPB4660wCe7OO6Fnz/jiF5HoJbWT\nWoogV9voBhuED/h774UFC6BfP7j7bth6a+jfH/r0CTOyW7SAY46B++4LM7X79AlFolKKQ1rajwuh\nHOWWlvxoT+qUatYs3D0cfzwsWhT2bmjZEnbbDXbcERo3jjtCEYmbmphERFJMTUwiIlJvKhAVJi1t\no4VQjuqmHOWWlvyoQIiISFbqgxARSTH1QYiISL2pQFSYtLSNFkI5qptylFta8qMCISIiWakPQkQk\nxdQHISIi9aYCUWHS0jZaCOWobspRbmnJjwqEiIhkpT4IEZEUUx+EiIjUmwpEhUlL22ghlKO6KUe5\npSU/KhAiIpKV+iBERFJMfRAiIlJvKhAVJi1to4VQjuqmHOWWlvyoQIiISFbqgxARSTH1QYiISL2p\nQFSYtLSNFkI5qptylFta8qMCISIiWakPQkQkxdQHISIi9aYCUWHS0jZaCOWobspRbmnJjwqEiIhk\npT4IEZEUUx+EiIjUmwpEhUlL22ghlKO6KUe5pSU/KhAiIpKV+iBERFJMfRAiIlJvKhAVJi1to4VQ\njuqmHOWWlvyoQIiISFbqgxARSTH1QYiISL2pQFSYtLSNFkI5qptylFta8qMCISIiWakPQkQkxdQH\nISIi9RZbgTCzY8zsbTNbbWb75jivh5nNMrPZZjaklDGWo7S0jRZCOaqbcpRbWvIT5x3EdOBnwAu1\nnWBmjYDhwE+BPYD+ZrZracIrT1OnTo07hMRTjuqmHOWWlvysH9cLu/u7AGaWte0rY39gjrvPz5z7\nANAHmFX8CMvTkiVL4g4h8ZSjuilHuaUlP0nvg9ga+KjG8wWZYyIiUmRFvYMws2eArWoeAhy4xN0f\nLeZrp9W8efPiDiHxlKO6KUe5pSU/sQ9zNbPngcHuPjnL7zoBl7t7j8zziwB392tquZbGuIqI1FNt\nw1xj64NYR239EJOAHc2sLfAJ0A/oX9tFavuPFBGR+otzmOtRZvYR0Al4zMyezBxvbWaPAbj7auBM\nYDwwA3jA3WfGFbOISJrE3sQkIiLJlPRRTJKDmd1pZovN7K0ax1qa2Xgze9fMnjazFnHGGCcz28bM\nnjOzGWY23cwGZY4rRxlmtqGZTTSzKZkcDc0cV45qMLNGZjbZzMZlnqciPyoQ5W0kYRJhTRcBE9x9\nF+A54OKSR5Ucq4Dz3H0P4ABgYGaipXKU4e4rgIPdvSPQAehpZvujHK3rbOCdGs9TkR8ViDLm7i8B\nX65zuA8wKvPzKOCokgaVIO6+yN2nZn5eCswEtkE5+g53X575cUPCwBVHOfovM9sGOBy4o8bhVORH\nBaLybOnuiyF8QAJbxhxPIphZO8I35NeArZSjtTLNJ1OARcAz7j4J5aim64ELCIWzWiryowJR+VI/\nCsHMmgFjgbMzdxLr5iTVOXL3NZkmpm2A/c1sD5QjAMysF7A4cyeaaxh9ReZHBaLyLDazrQDM7AfA\n/8UcT6zMbH1Ccfibu/8zc1g5ysLd/w1UAT1Qjqp1Bo40sw+A+4FDzOxvwKI05EcFovwZ3/1mMw4Y\nkPn5JOCf6/6FlLkLeMfdb6xxTDnKMLNW1SNwzKwpcCihr0Y5Atz9d+6+nbvvQJio+5y7nwA8Sgry\no3kQZczM7gO6AZsDi4GhwCPA34FtgfnAce6ejqUn12FmnYEXCUvLe+bxO+B14EGUI8xsL0Ina6PM\nY4y7/8HMNkM5+g4z60pYFujItORHBUJERLJSE5OIiGSlAiEiIlmpQIiISFYqECIikpUKhIiIZKUC\nISIiWalAiETAzL6OOwaRqKlAiERDE4qk4qhAiBSJmfU2s9fM7M3M5jJbZI63yjyfbma3m9m8zMxc\nkURRgRApnn+5eyd33w8YA1yYOT4UeNbd9yIsJLhtXAGK5LJ+3AGIVLBtzexBoDWwATA3c7wLmQ1m\n3P1pM1t30yeRRNAdhEjx3Azc5O57A78BmtRyXq59BkRiowIhEo1sH/KbAAszP59U4/jLQF8AMzsM\n2LS4oYk0jFZzFYmAma0iFAMjjGi6DngfuAH4grCx/Y/c/ZBMZ/V9wFbAq0BvoJ27r4wjdpHaqECI\nlJiZNQZWu/tqM+sE/MXd9407LpF1qZNapPS2Ax40s0bACuDUmOMRyUp3ECIikpU6qUVEJCsVCBER\nyUoFQkREslKBEBGRrFQgREQkKxUIERHJ6v8DkLkDeDetCHwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9975f6f2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "autocorrelation_plot(train[1:48])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there is a significant positive correlation with the first 4 or 5 lags. That may be a good starting point for the AR parameter (p) of the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot of energy load over time (see above) shows that the time series is not stationary due to its seasonality (daily peaks and also peaks in August and February due to the increased energy usage). This suggests the a certain degree of differencing the data might be necessary. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hFvHPPut9rFuUFGCcFKAwTD10DAkRrFFSToZJlmGUlBDBfG8AsiMM0xIlPXhQxOyiRd7V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\nc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ygERQAAAABoKcMqGLr705J+KunS3EW/lnSkmV1gZiPN7M2S5ipUFuXueyVdp7B4zUSF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\n/dHVU+uJM0oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f99764e3c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['load'].diff(periods=24).plot(y='load', figsize=(15, 8), fontsize=12)\n",
    "plt.xlabel('timestamp', fontsize=12)\n",
    "plt.ylabel('load', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even after differencing the data by 24 hours (the seasonal frequency), we still see a seasonal trend in the data. \n",
    "\n",
    "Selecting the best parameters for an Arima model can be challenging - somewhat subjective and time intesive, so we'll leave it as an exercise to the user. We used an **auto_arima()** function to search a provided space of parameters for the best model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model search takes a while, so don't run it during the demo\n",
    "auto_tune = False\n",
    "\n",
    "if (auto_tune):\n",
    "    auto_model = auto_arima(train, start_p=1, start_q=0,\n",
    "                               max_p=5, max_q=0, m=24,\n",
    "                               start_P=0, max_P=2,Q=0, \n",
    "                               seasonal=True,\n",
    "                               d=1, D=1, trace=True,\n",
    "                               error_action='ignore',  \n",
    "                               suppress_warnings=True, \n",
    "                               stepwise=True)\n",
    "    print(auto_model.aic())\n",
    "    print(auto_model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 Statespace Model Results                                 \n",
      "==========================================================================================\n",
      "Dep. Variable:                               load   No. Observations:                  720\n",
      "Model:             SARIMAX(4, 1, 0)x(1, 1, 0, 24)   Log Likelihood                1619.963\n",
      "Date:                            Fri, 28 Sep 2018   AIC                          -3227.926\n",
      "Time:                                    13:35:01   BIC                          -3200.663\n",
      "Sample:                                11-01-2014   HQIC                         -3217.384\n",
      "                                     - 11-30-2014                                         \n",
      "Covariance Type:                              opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1          0.7859      0.024     32.971      0.000       0.739       0.833\n",
      "ar.L2         -0.4365      0.047     -9.299      0.000      -0.528      -0.344\n",
      "ar.L3          0.1201      0.061      1.955      0.051      -0.000       0.240\n",
      "ar.L4         -0.0594      0.050     -1.182      0.237      -0.158       0.039\n",
      "ar.S.L24      -0.2410      0.033     -7.306      0.000      -0.306      -0.176\n",
      "sigma2         0.0006   1.77e-05     31.121      0.000       0.001       0.001\n",
      "===================================================================================\n",
      "Ljung-Box (Q):                       52.26   Jarque-Bera (JB):               466.67\n",
      "Prob(Q):                              0.09   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               1.17   Skew:                             0.02\n",
      "Prob(H) (two-sided):                  0.23   Kurtosis:                         7.01\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "order = (4, 1, 0)\n",
    "seasonal_order = (1, 1, 0, 24)\n",
    "\n",
    "model = SARIMAX(endog=train, order=order, seasonal_order=seasonal_order)\n",
    "results = model.fit()\n",
    "\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we display the distribution of residuals. A zero mean in the residuals may indicate that there is no bias in the prediction. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/LU5/7lyJP446SnqVZcOt+Fs/8+Y7BnPkYzBpkrP4b9ok52HgQHlM1NX92zn/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\nShGi4q8oilKE/H9kMxlMIj5HggAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9976537a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OSWDdurDgWJ8+sMceYY/hUaNC1fuKK8Jr/ZJIXu20U9g/+8wzw8/+88+HCW5z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\nEQHg/wGfiLHnwQlJKgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f997650fc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           0\n",
      "count 100.00\n",
      "mean    0.00\n",
      "std     0.04\n",
      "min    -0.13\n",
      "25%    -0.01\n",
      "50%    -0.00\n",
      "75%     0.02\n",
      "max     0.13\n"
     ]
    }
   ],
   "source": [
    "# plot residual errors\n",
    "residuals = pd.DataFrame(results.resid[0:100])\n",
    "residuals.plot()\n",
    "plt.show()\n",
    "residuals.plot(kind='kde')\n",
    "plt.show()\n",
    "print(residuals.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will perform the so-called **walk forward validation**. In practice, time series models are re-trained each time a new data becomes available. This allows the model to make the best forecast at each time step. \n",
    "\n",
    "Starting at the beginning of the time series, we train the model on the train data set. Then we make a prediction on the next time step. The prediction is then evaluated against the known value. The training set is then expanded to include the known value and the process is repeated. (Note that we keep the training set window fixed, for more efficient training, so every time we add a new observation to the training set, we remove the observation from the beginning of the set.)\n",
    "\n",
    "This process provides a more robust estimation of how the model will perform in practice. However, it comes at the computation cost of creating so many models. This is acceptable if the data is small or if the model is simple, but could be an issue at scale. \n",
    "\n",
    "Walk-forward validation is the gold standard of time series model evaluation and is recommended for your own projects."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a test data point for each HORIZON step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "      <th>load+1</th>\n",
       "      <th>load+2</th>\n",
       "      <th>load+3</th>\n",
       "      <th>load+4</th>\n",
       "      <th>load+5</th>\n",
       "      <th>load+6</th>\n",
       "      <th>load+7</th>\n",
       "      <th>load+8</th>\n",
       "      <th>load+9</th>\n",
       "      <th>...</th>\n",
       "      <th>load+14</th>\n",
       "      <th>load+15</th>\n",
       "      <th>load+16</th>\n",
       "      <th>load+17</th>\n",
       "      <th>load+18</th>\n",
       "      <th>load+19</th>\n",
       "      <th>load+20</th>\n",
       "      <th>load+21</th>\n",
       "      <th>load+22</th>\n",
       "      <th>load+23</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-12-01 00:00:00</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.66</td>\n",
       "      <td>...</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 01:00:00</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.69</td>\n",
       "      <td>...</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 02:00:00</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.67</td>\n",
       "      <td>...</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 03:00:00</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.66</td>\n",
       "      <td>...</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 04:00:00</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.66</td>\n",
       "      <td>...</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     load  load+1  load+2  load+3  load+4  load+5  load+6  \\\n",
       "2014-12-01 00:00:00  0.15    0.11    0.10    0.10    0.15    0.30    0.54   \n",
       "2014-12-01 01:00:00  0.11    0.10    0.10    0.15    0.30    0.54    0.65   \n",
       "2014-12-01 02:00:00  0.10    0.10    0.15    0.30    0.54    0.65    0.66   \n",
       "2014-12-01 03:00:00  0.10    0.15    0.30    0.54    0.65    0.66    0.66   \n",
       "2014-12-01 04:00:00  0.15    0.30    0.54    0.65    0.66    0.66    0.69   \n",
       "\n",
       "                     load+7  load+8  load+9   ...     load+14  load+15  \\\n",
       "2014-12-01 00:00:00    0.65    0.66    0.66   ...        0.65     0.67   \n",
       "2014-12-01 01:00:00    0.66    0.66    0.69   ...        0.67     0.81   \n",
       "2014-12-01 02:00:00    0.66    0.69    0.67   ...        0.81     0.92   \n",
       "2014-12-01 03:00:00    0.69    0.67    0.66   ...        0.92     0.92   \n",
       "2014-12-01 04:00:00    0.67    0.66    0.66   ...        0.92     0.86   \n",
       "\n",
       "                     load+16  load+17  load+18  load+19  load+20  load+21  \\\n",
       "2014-12-01 00:00:00     0.81     0.92     0.92     0.86     0.80     0.66   \n",
       "2014-12-01 01:00:00     0.92     0.92     0.86     0.80     0.66     0.50   \n",
       "2014-12-01 02:00:00     0.92     0.86     0.80     0.66     0.50     0.37   \n",
       "2014-12-01 03:00:00     0.86     0.80     0.66     0.50     0.37     0.29   \n",
       "2014-12-01 04:00:00     0.80     0.66     0.50     0.37     0.29     0.26   \n",
       "\n",
       "                     load+22  load+23  \n",
       "2014-12-01 00:00:00     0.50     0.37  \n",
       "2014-12-01 01:00:00     0.37     0.29  \n",
       "2014-12-01 02:00:00     0.29     0.26  \n",
       "2014-12-01 03:00:00     0.26     0.24  \n",
       "2014-12-01 04:00:00     0.24     0.26  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_shifted = test.copy()\n",
    "\n",
    "for t in range(1, HORIZON):\n",
    "    test_shifted['load+'+str(t)] = test_shifted['load'].shift(-t, freq='H')\n",
    "    \n",
    "test_shifted = test_shifted.dropna(how='any')\n",
    "test_shifted.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make predictions on the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 . Predicting time step:  2014-12-01 00:00:00\n",
      "2 . Predicting time step:  2014-12-01 01:00:00\n",
      "3 . Predicting time step:  2014-12-01 02:00:00\n",
      "4 . Predicting time step:  2014-12-01 03:00:00\n",
      "5 . Predicting time step:  2014-12-01 04:00:00\n",
      "6 . Predicting time step:  2014-12-01 05:00:00\n",
      "7 . Predicting time step:  2014-12-01 06:00:00\n",
      "8 . Predicting time step:  2014-12-01 07:00:00\n",
      "9 . Predicting time step:  2014-12-01 08:00:00\n",
      "10 . Predicting time step:  2014-12-01 09:00:00\n",
      "11 . Predicting time step:  2014-12-01 10:00:00\n",
      "12 . Predicting time step:  2014-12-01 11:00:00\n",
      "13 . Predicting time step:  2014-12-01 12:00:00\n",
      "14 . Predicting time step:  2014-12-01 13:00:00\n",
      "15 . Predicting time step:  2014-12-01 14:00:00\n",
      "16 . Predicting time step:  2014-12-01 15:00:00\n",
      "17 . Predicting time step:  2014-12-01 16:00:00\n",
      "18 . Predicting time step:  2014-12-01 17:00:00\n",
      "19 . Predicting time step:  2014-12-01 18:00:00\n",
      "20 . Predicting time step:  2014-12-01 19:00:00\n",
      "21 . Predicting time step:  2014-12-01 20:00:00\n",
      "22 . Predicting time step:  2014-12-01 21:00:00\n",
      "23 . Predicting time step:  2014-12-01 22:00:00\n",
      "24 . Predicting time step:  2014-12-01 23:00:00\n",
      "25 . Predicting time step:  2014-12-02 00:00:00\n",
      "26 . Predicting time step:  2014-12-02 01:00:00\n",
      "27 . Predicting time step:  2014-12-02 02:00:00\n",
      "28 . Predicting time step:  2014-12-02 03:00:00\n",
      "29 . Predicting time step:  2014-12-02 04:00:00\n",
      "30 . Predicting time step:  2014-12-02 05:00:00\n",
      "31 . Predicting time step:  2014-12-02 06:00:00\n",
      "32 . Predicting time step:  2014-12-02 07:00:00\n",
      "33 . Predicting time step:  2014-12-02 08:00:00\n",
      "34 . Predicting time step:  2014-12-02 09:00:00\n",
      "35 . Predicting time step:  2014-12-02 10:00:00\n",
      "36 . Predicting time step:  2014-12-02 11:00:00\n",
      "37 . Predicting time step:  2014-12-02 12:00:00\n",
      "38 . Predicting time step:  2014-12-02 13:00:00\n",
      "39 . Predicting time step:  2014-12-02 14:00:00\n",
      "40 . Predicting time step:  2014-12-02 15:00:00\n",
      "41 . Predicting time step:  2014-12-02 16:00:00\n",
      "42 . Predicting time step:  2014-12-02 17:00:00\n",
      "43 . Predicting time step:  2014-12-02 18:00:00\n",
      "44 . Predicting time step:  2014-12-02 19:00:00\n",
      "45 . Predicting time step:  2014-12-02 20:00:00\n",
      "46 . Predicting time step:  2014-12-02 21:00:00\n",
      "47 . Predicting time step:  2014-12-02 22:00:00\n",
      "48 . Predicting time step:  2014-12-02 23:00:00\n",
      "49 . Predicting time step:  2014-12-03 00:00:00\n",
      "50 . Predicting time step:  2014-12-03 01:00:00\n",
      "51 . Predicting time step:  2014-12-03 02:00:00\n",
      "52 . Predicting time step:  2014-12-03 03:00:00\n",
      "53 . Predicting time step:  2014-12-03 04:00:00\n",
      "54 . Predicting time step:  2014-12-03 05:00:00\n",
      "55 . Predicting time step:  2014-12-03 06:00:00\n",
      "56 . Predicting time step:  2014-12-03 07:00:00\n",
      "57 . Predicting time step:  2014-12-03 08:00:00\n",
      "58 . Predicting time step:  2014-12-03 09:00:00\n",
      "59 . Predicting time step:  2014-12-03 10:00:00\n",
      "60 . Predicting time step:  2014-12-03 11:00:00\n",
      "61 . Predicting time step:  2014-12-03 12:00:00\n",
      "62 . Predicting time step:  2014-12-03 13:00:00\n",
      "63 . Predicting time step:  2014-12-03 14:00:00\n",
      "64 . Predicting time step:  2014-12-03 15:00:00\n",
      "65 . Predicting time step:  2014-12-03 16:00:00\n",
      "66 . Predicting time step:  2014-12-03 17:00:00\n",
      "67 . Predicting time step:  2014-12-03 18:00:00\n",
      "68 . Predicting time step:  2014-12-03 19:00:00\n",
      "69 . Predicting time step:  2014-12-03 20:00:00\n",
      "70 . Predicting time step:  2014-12-03 21:00:00\n",
      "71 . Predicting time step:  2014-12-03 22:00:00\n",
      "72 . Predicting time step:  2014-12-03 23:00:00\n",
      "73 . Predicting time step:  2014-12-04 00:00:00\n",
      "74 . Predicting time step:  2014-12-04 01:00:00\n",
      "75 . Predicting time step:  2014-12-04 02:00:00\n",
      "76 . Predicting time step:  2014-12-04 03:00:00\n",
      "77 . Predicting time step:  2014-12-04 04:00:00\n",
      "78 . Predicting time step:  2014-12-04 05:00:00\n",
      "79 . Predicting time step:  2014-12-04 06:00:00\n",
      "80 . Predicting time step:  2014-12-04 07:00:00\n",
      "81 . Predicting time step:  2014-12-04 08:00:00\n",
      "82 . Predicting time step:  2014-12-04 09:00:00\n",
      "83 . Predicting time step:  2014-12-04 10:00:00\n",
      "84 . Predicting time step:  2014-12-04 11:00:00\n",
      "85 . Predicting time step:  2014-12-04 12:00:00\n",
      "86 . Predicting time step:  2014-12-04 13:00:00\n",
      "87 . Predicting time step:  2014-12-04 14:00:00\n",
      "88 . Predicting time step:  2014-12-04 15:00:00\n",
      "89 . Predicting time step:  2014-12-04 16:00:00\n",
      "90 . Predicting time step:  2014-12-04 17:00:00\n",
      "91 . Predicting time step:  2014-12-04 18:00:00\n",
      "92 . Predicting time step:  2014-12-04 19:00:00\n",
      "93 . Predicting time step:  2014-12-04 20:00:00\n",
      "94 . Predicting time step:  2014-12-04 21:00:00\n",
      "95 . Predicting time step:  2014-12-04 22:00:00\n",
      "96 . Predicting time step:  2014-12-04 23:00:00\n",
      "97 . Predicting time step:  2014-12-05 00:00:00\n",
      "98 . Predicting time step:  2014-12-05 01:00:00\n",
      "99 . Predicting time step:  2014-12-05 02:00:00\n",
      "100 . Predicting time step:  2014-12-05 03:00:00\n",
      "101 . Predicting time step:  2014-12-05 04:00:00\n",
      "102 . Predicting time step:  2014-12-05 05:00:00\n",
      "103 . Predicting time step:  2014-12-05 06:00:00\n",
      "104 . Predicting time step:  2014-12-05 07:00:00\n",
      "105 . Predicting time step:  2014-12-05 08:00:00\n",
      "106 . Predicting time step:  2014-12-05 09:00:00\n",
      "107 . Predicting time step:  2014-12-05 10:00:00\n",
      "108 . Predicting time step:  2014-12-05 11:00:00\n",
      "109 . Predicting time step:  2014-12-05 12:00:00\n",
      "110 . Predicting time step:  2014-12-05 13:00:00\n",
      "111 . Predicting time step:  2014-12-05 14:00:00\n",
      "112 . Predicting time step:  2014-12-05 15:00:00\n",
      "113 . Predicting time step:  2014-12-05 16:00:00\n",
      "114 . Predicting time step:  2014-12-05 17:00:00\n",
      "115 . Predicting time step:  2014-12-05 18:00:00\n",
      "116 . Predicting time step:  2014-12-05 19:00:00\n",
      "117 . Predicting time step:  2014-12-05 20:00:00\n",
      "118 . Predicting time step:  2014-12-05 21:00:00\n",
      "119 . Predicting time step:  2014-12-05 22:00:00\n",
      "120 . Predicting time step:  2014-12-05 23:00:00\n",
      "121 . Predicting time step:  2014-12-06 00:00:00\n",
      "122 . Predicting time step:  2014-12-06 01:00:00\n",
      "123 . Predicting time step:  2014-12-06 02:00:00\n",
      "124 . Predicting time step:  2014-12-06 03:00:00\n",
      "125 . Predicting time step:  2014-12-06 04:00:00\n",
      "126 . Predicting time step:  2014-12-06 05:00:00\n",
      "127 . Predicting time step:  2014-12-06 06:00:00\n",
      "128 . Predicting time step:  2014-12-06 07:00:00\n",
      "129 . Predicting time step:  2014-12-06 08:00:00\n",
      "130 . Predicting time step:  2014-12-06 09:00:00\n",
      "131 . Predicting time step:  2014-12-06 10:00:00\n",
      "132 . Predicting time step:  2014-12-06 11:00:00\n",
      "133 . Predicting time step:  2014-12-06 12:00:00\n",
      "134 . Predicting time step:  2014-12-06 13:00:00\n",
      "135 . Predicting time step:  2014-12-06 14:00:00\n",
      "136 . Predicting time step:  2014-12-06 15:00:00\n",
      "137 . Predicting time step:  2014-12-06 16:00:00\n",
      "138 . Predicting time step:  2014-12-06 17:00:00\n",
      "139 . Predicting time step:  2014-12-06 18:00:00\n",
      "140 . Predicting time step:  2014-12-06 19:00:00\n",
      "141 . Predicting time step:  2014-12-06 20:00:00\n",
      "142 . Predicting time step:  2014-12-06 21:00:00\n",
      "143 . Predicting time step:  2014-12-06 22:00:00\n",
      "144 . Predicting time step:  2014-12-06 23:00:00\n",
      "145 . Predicting time step:  2014-12-07 00:00:00\n",
      "146 . Predicting time step:  2014-12-07 01:00:00\n",
      "147 . Predicting time step:  2014-12-07 02:00:00\n",
      "148 . Predicting time step:  2014-12-07 03:00:00\n",
      "149 . Predicting time step:  2014-12-07 04:00:00\n",
      "150 . Predicting time step:  2014-12-07 05:00:00\n",
      "151 . Predicting time step:  2014-12-07 06:00:00\n",
      "152 . Predicting time step:  2014-12-07 07:00:00\n",
      "153 . Predicting time step:  2014-12-07 08:00:00\n",
      "154 . Predicting time step:  2014-12-07 09:00:00\n",
      "155 . Predicting time step:  2014-12-07 10:00:00\n",
      "156 . Predicting time step:  2014-12-07 11:00:00\n",
      "157 . Predicting time step:  2014-12-07 12:00:00\n",
      "158 . Predicting time step:  2014-12-07 13:00:00\n",
      "159 . Predicting time step:  2014-12-07 14:00:00\n",
      "160 . Predicting time step:  2014-12-07 15:00:00\n",
      "161 . Predicting time step:  2014-12-07 16:00:00\n",
      "162 . Predicting time step:  2014-12-07 17:00:00\n",
      "163 . Predicting time step:  2014-12-07 18:00:00\n",
      "164 . Predicting time step:  2014-12-07 19:00:00\n",
      "165 . Predicting time step:  2014-12-07 20:00:00\n",
      "166 . Predicting time step:  2014-12-07 21:00:00\n",
      "167 . Predicting time step:  2014-12-07 22:00:00\n",
      "168 . Predicting time step:  2014-12-07 23:00:00\n",
      "169 . Predicting time step:  2014-12-08 00:00:00\n",
      "170 . Predicting time step:  2014-12-08 01:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "171 . Predicting time step:  2014-12-08 02:00:00\n",
      "172 . Predicting time step:  2014-12-08 03:00:00\n",
      "173 . Predicting time step:  2014-12-08 04:00:00\n",
      "174 . Predicting time step:  2014-12-08 05:00:00\n",
      "175 . Predicting time step:  2014-12-08 06:00:00\n",
      "176 . Predicting time step:  2014-12-08 07:00:00\n",
      "177 . Predicting time step:  2014-12-08 08:00:00\n",
      "178 . Predicting time step:  2014-12-08 09:00:00\n",
      "179 . Predicting time step:  2014-12-08 10:00:00\n",
      "180 . Predicting time step:  2014-12-08 11:00:00\n",
      "181 . Predicting time step:  2014-12-08 12:00:00\n",
      "182 . Predicting time step:  2014-12-08 13:00:00\n",
      "183 . Predicting time step:  2014-12-08 14:00:00\n",
      "184 . Predicting time step:  2014-12-08 15:00:00\n",
      "185 . Predicting time step:  2014-12-08 16:00:00\n",
      "186 . Predicting time step:  2014-12-08 17:00:00\n",
      "187 . Predicting time step:  2014-12-08 18:00:00\n",
      "188 . Predicting time step:  2014-12-08 19:00:00\n",
      "189 . Predicting time step:  2014-12-08 20:00:00\n",
      "190 . Predicting time step:  2014-12-08 21:00:00\n",
      "191 . Predicting time step:  2014-12-08 22:00:00\n",
      "192 . Predicting time step:  2014-12-08 23:00:00\n",
      "193 . Predicting time step:  2014-12-09 00:00:00\n",
      "194 . Predicting time step:  2014-12-09 01:00:00\n",
      "195 . Predicting time step:  2014-12-09 02:00:00\n",
      "196 . Predicting time step:  2014-12-09 03:00:00\n",
      "197 . Predicting time step:  2014-12-09 04:00:00\n",
      "198 . Predicting time step:  2014-12-09 05:00:00\n",
      "199 . Predicting time step:  2014-12-09 06:00:00\n",
      "200 . Predicting time step:  2014-12-09 07:00:00\n",
      "201 . Predicting time step:  2014-12-09 08:00:00\n",
      "202 . Predicting time step:  2014-12-09 09:00:00\n",
      "203 . Predicting time step:  2014-12-09 10:00:00\n",
      "204 . Predicting time step:  2014-12-09 11:00:00\n",
      "205 . Predicting time step:  2014-12-09 12:00:00\n",
      "206 . Predicting time step:  2014-12-09 13:00:00\n",
      "207 . Predicting time step:  2014-12-09 14:00:00\n",
      "208 . Predicting time step:  2014-12-09 15:00:00\n",
      "209 . Predicting time step:  2014-12-09 16:00:00\n",
      "210 . Predicting time step:  2014-12-09 17:00:00\n",
      "211 . Predicting time step:  2014-12-09 18:00:00\n",
      "212 . Predicting time step:  2014-12-09 19:00:00\n",
      "213 . Predicting time step:  2014-12-09 20:00:00\n",
      "214 . Predicting time step:  2014-12-09 21:00:00\n",
      "215 . Predicting time step:  2014-12-09 22:00:00\n",
      "216 . Predicting time step:  2014-12-09 23:00:00\n",
      "217 . Predicting time step:  2014-12-10 00:00:00\n",
      "218 . Predicting time step:  2014-12-10 01:00:00\n",
      "219 . Predicting time step:  2014-12-10 02:00:00\n",
      "220 . Predicting time step:  2014-12-10 03:00:00\n",
      "221 . Predicting time step:  2014-12-10 04:00:00\n",
      "222 . Predicting time step:  2014-12-10 05:00:00\n",
      "223 . Predicting time step:  2014-12-10 06:00:00\n",
      "224 . Predicting time step:  2014-12-10 07:00:00\n",
      "225 . Predicting time step:  2014-12-10 08:00:00\n",
      "226 . Predicting time step:  2014-12-10 09:00:00\n",
      "227 . Predicting time step:  2014-12-10 10:00:00\n",
      "228 . Predicting time step:  2014-12-10 11:00:00\n",
      "229 . Predicting time step:  2014-12-10 12:00:00\n",
      "230 . Predicting time step:  2014-12-10 13:00:00\n",
      "231 . Predicting time step:  2014-12-10 14:00:00\n",
      "232 . Predicting time step:  2014-12-10 15:00:00\n",
      "233 . Predicting time step:  2014-12-10 16:00:00\n",
      "234 . Predicting time step:  2014-12-10 17:00:00\n",
      "235 . Predicting time step:  2014-12-10 18:00:00\n",
      "236 . Predicting time step:  2014-12-10 19:00:00\n",
      "237 . Predicting time step:  2014-12-10 20:00:00\n",
      "238 . Predicting time step:  2014-12-10 21:00:00\n",
      "239 . Predicting time step:  2014-12-10 22:00:00\n",
      "240 . Predicting time step:  2014-12-10 23:00:00\n",
      "241 . Predicting time step:  2014-12-11 00:00:00\n",
      "242 . Predicting time step:  2014-12-11 01:00:00\n",
      "243 . Predicting time step:  2014-12-11 02:00:00\n",
      "244 . Predicting time step:  2014-12-11 03:00:00\n",
      "245 . Predicting time step:  2014-12-11 04:00:00\n",
      "246 . Predicting time step:  2014-12-11 05:00:00\n",
      "247 . Predicting time step:  2014-12-11 06:00:00\n",
      "248 . Predicting time step:  2014-12-11 07:00:00\n",
      "249 . Predicting time step:  2014-12-11 08:00:00\n",
      "250 . Predicting time step:  2014-12-11 09:00:00\n",
      "251 . Predicting time step:  2014-12-11 10:00:00\n",
      "252 . Predicting time step:  2014-12-11 11:00:00\n",
      "253 . Predicting time step:  2014-12-11 12:00:00\n",
      "254 . Predicting time step:  2014-12-11 13:00:00\n",
      "255 . Predicting time step:  2014-12-11 14:00:00\n",
      "256 . Predicting time step:  2014-12-11 15:00:00\n",
      "257 . Predicting time step:  2014-12-11 16:00:00\n",
      "258 . Predicting time step:  2014-12-11 17:00:00\n",
      "259 . Predicting time step:  2014-12-11 18:00:00\n",
      "260 . Predicting time step:  2014-12-11 19:00:00\n",
      "261 . Predicting time step:  2014-12-11 20:00:00\n",
      "262 . Predicting time step:  2014-12-11 21:00:00\n",
      "263 . Predicting time step:  2014-12-11 22:00:00\n",
      "264 . Predicting time step:  2014-12-11 23:00:00\n",
      "265 . Predicting time step:  2014-12-12 00:00:00\n",
      "266 . Predicting time step:  2014-12-12 01:00:00\n",
      "267 . Predicting time step:  2014-12-12 02:00:00\n",
      "268 . Predicting time step:  2014-12-12 03:00:00\n",
      "269 . Predicting time step:  2014-12-12 04:00:00\n",
      "270 . Predicting time step:  2014-12-12 05:00:00\n",
      "271 . Predicting time step:  2014-12-12 06:00:00\n",
      "272 . Predicting time step:  2014-12-12 07:00:00\n",
      "273 . Predicting time step:  2014-12-12 08:00:00\n",
      "274 . Predicting time step:  2014-12-12 09:00:00\n",
      "275 . Predicting time step:  2014-12-12 10:00:00\n",
      "276 . Predicting time step:  2014-12-12 11:00:00\n",
      "277 . Predicting time step:  2014-12-12 12:00:00\n",
      "278 . Predicting time step:  2014-12-12 13:00:00\n",
      "279 . Predicting time step:  2014-12-12 14:00:00\n",
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      "289 . Predicting time step:  2014-12-13 00:00:00\n",
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      "302 . Predicting time step:  2014-12-13 13:00:00\n",
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      "304 . Predicting time step:  2014-12-13 15:00:00\n",
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      "313 . Predicting time step:  2014-12-14 00:00:00\n",
      "314 . Predicting time step:  2014-12-14 01:00:00\n",
      "315 . Predicting time step:  2014-12-14 02:00:00\n",
      "316 . Predicting time step:  2014-12-14 03:00:00\n",
      "317 . Predicting time step:  2014-12-14 04:00:00\n",
      "318 . Predicting time step:  2014-12-14 05:00:00\n",
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      "329 . Predicting time step:  2014-12-14 16:00:00\n",
      "330 . Predicting time step:  2014-12-14 17:00:00\n",
      "331 . Predicting time step:  2014-12-14 18:00:00\n",
      "332 . Predicting time step:  2014-12-14 19:00:00\n",
      "333 . Predicting time step:  2014-12-14 20:00:00\n",
      "334 . Predicting time step:  2014-12-14 21:00:00\n",
      "335 . Predicting time step:  2014-12-14 22:00:00\n",
      "336 . Predicting time step:  2014-12-14 23:00:00\n",
      "337 . Predicting time step:  2014-12-15 00:00:00\n",
      "338 . Predicting time step:  2014-12-15 01:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "339 . Predicting time step:  2014-12-15 02:00:00\n",
      "340 . Predicting time step:  2014-12-15 03:00:00\n",
      "341 . Predicting time step:  2014-12-15 04:00:00\n",
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      "356 . Predicting time step:  2014-12-15 19:00:00\n",
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      "358 . Predicting time step:  2014-12-15 21:00:00\n",
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      "361 . Predicting time step:  2014-12-16 00:00:00\n",
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      "505 . Predicting time step:  2014-12-22 00:00:00\n",
      "506 . Predicting time step:  2014-12-22 01:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "507 . Predicting time step:  2014-12-22 02:00:00\n",
      "508 . Predicting time step:  2014-12-22 03:00:00\n",
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      "599 . Predicting time step:  2014-12-25 22:00:00\n",
      "600 . Predicting time step:  2014-12-25 23:00:00\n",
      "601 . Predicting time step:  2014-12-26 00:00:00\n",
      "602 . Predicting time step:  2014-12-26 01:00:00\n",
      "603 . Predicting time step:  2014-12-26 02:00:00\n",
      "604 . Predicting time step:  2014-12-26 03:00:00\n",
      "605 . Predicting time step:  2014-12-26 04:00:00\n",
      "606 . Predicting time step:  2014-12-26 05:00:00\n",
      "607 . Predicting time step:  2014-12-26 06:00:00\n",
      "608 . Predicting time step:  2014-12-26 07:00:00\n",
      "609 . Predicting time step:  2014-12-26 08:00:00\n",
      "610 . Predicting time step:  2014-12-26 09:00:00\n",
      "611 . Predicting time step:  2014-12-26 10:00:00\n",
      "612 . Predicting time step:  2014-12-26 11:00:00\n",
      "613 . Predicting time step:  2014-12-26 12:00:00\n",
      "614 . Predicting time step:  2014-12-26 13:00:00\n",
      "615 . Predicting time step:  2014-12-26 14:00:00\n",
      "616 . Predicting time step:  2014-12-26 15:00:00\n",
      "617 . Predicting time step:  2014-12-26 16:00:00\n",
      "618 . Predicting time step:  2014-12-26 17:00:00\n",
      "619 . Predicting time step:  2014-12-26 18:00:00\n",
      "620 . Predicting time step:  2014-12-26 19:00:00\n",
      "621 . Predicting time step:  2014-12-26 20:00:00\n",
      "622 . Predicting time step:  2014-12-26 21:00:00\n",
      "623 . Predicting time step:  2014-12-26 22:00:00\n",
      "624 . Predicting time step:  2014-12-26 23:00:00\n",
      "625 . Predicting time step:  2014-12-27 00:00:00\n",
      "626 . Predicting time step:  2014-12-27 01:00:00\n",
      "627 . Predicting time step:  2014-12-27 02:00:00\n",
      "628 . Predicting time step:  2014-12-27 03:00:00\n",
      "629 . Predicting time step:  2014-12-27 04:00:00\n",
      "630 . Predicting time step:  2014-12-27 05:00:00\n",
      "631 . Predicting time step:  2014-12-27 06:00:00\n",
      "632 . Predicting time step:  2014-12-27 07:00:00\n",
      "633 . Predicting time step:  2014-12-27 08:00:00\n",
      "634 . Predicting time step:  2014-12-27 09:00:00\n",
      "635 . Predicting time step:  2014-12-27 10:00:00\n",
      "636 . Predicting time step:  2014-12-27 11:00:00\n",
      "637 . Predicting time step:  2014-12-27 12:00:00\n",
      "638 . Predicting time step:  2014-12-27 13:00:00\n",
      "639 . Predicting time step:  2014-12-27 14:00:00\n",
      "640 . Predicting time step:  2014-12-27 15:00:00\n",
      "641 . Predicting time step:  2014-12-27 16:00:00\n",
      "642 . Predicting time step:  2014-12-27 17:00:00\n",
      "643 . Predicting time step:  2014-12-27 18:00:00\n",
      "644 . Predicting time step:  2014-12-27 19:00:00\n",
      "645 . Predicting time step:  2014-12-27 20:00:00\n",
      "646 . Predicting time step:  2014-12-27 21:00:00\n",
      "647 . Predicting time step:  2014-12-27 22:00:00\n",
      "648 . Predicting time step:  2014-12-27 23:00:00\n",
      "649 . Predicting time step:  2014-12-28 00:00:00\n",
      "650 . Predicting time step:  2014-12-28 01:00:00\n",
      "651 . Predicting time step:  2014-12-28 02:00:00\n",
      "652 . Predicting time step:  2014-12-28 03:00:00\n",
      "653 . Predicting time step:  2014-12-28 04:00:00\n",
      "654 . Predicting time step:  2014-12-28 05:00:00\n",
      "655 . Predicting time step:  2014-12-28 06:00:00\n",
      "656 . Predicting time step:  2014-12-28 07:00:00\n",
      "657 . Predicting time step:  2014-12-28 08:00:00\n",
      "658 . Predicting time step:  2014-12-28 09:00:00\n",
      "659 . Predicting time step:  2014-12-28 10:00:00\n",
      "660 . Predicting time step:  2014-12-28 11:00:00\n",
      "661 . Predicting time step:  2014-12-28 12:00:00\n",
      "662 . Predicting time step:  2014-12-28 13:00:00\n",
      "663 . Predicting time step:  2014-12-28 14:00:00\n",
      "664 . Predicting time step:  2014-12-28 15:00:00\n",
      "665 . Predicting time step:  2014-12-28 16:00:00\n",
      "666 . Predicting time step:  2014-12-28 17:00:00\n",
      "667 . Predicting time step:  2014-12-28 18:00:00\n",
      "668 . Predicting time step:  2014-12-28 19:00:00\n",
      "669 . Predicting time step:  2014-12-28 20:00:00\n",
      "670 . Predicting time step:  2014-12-28 21:00:00\n",
      "671 . Predicting time step:  2014-12-28 22:00:00\n",
      "672 . Predicting time step:  2014-12-28 23:00:00\n",
      "673 . Predicting time step:  2014-12-29 00:00:00\n",
      "674 . Predicting time step:  2014-12-29 01:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "675 . Predicting time step:  2014-12-29 02:00:00\n",
      "676 . Predicting time step:  2014-12-29 03:00:00\n",
      "677 . Predicting time step:  2014-12-29 04:00:00\n",
      "678 . Predicting time step:  2014-12-29 05:00:00\n",
      "679 . Predicting time step:  2014-12-29 06:00:00\n",
      "680 . Predicting time step:  2014-12-29 07:00:00\n",
      "681 . Predicting time step:  2014-12-29 08:00:00\n",
      "682 . Predicting time step:  2014-12-29 09:00:00\n",
      "683 . Predicting time step:  2014-12-29 10:00:00\n",
      "684 . Predicting time step:  2014-12-29 11:00:00\n",
      "685 . Predicting time step:  2014-12-29 12:00:00\n",
      "686 . Predicting time step:  2014-12-29 13:00:00\n",
      "687 . Predicting time step:  2014-12-29 14:00:00\n",
      "688 . Predicting time step:  2014-12-29 15:00:00\n",
      "689 . Predicting time step:  2014-12-29 16:00:00\n",
      "690 . Predicting time step:  2014-12-29 17:00:00\n",
      "691 . Predicting time step:  2014-12-29 18:00:00\n",
      "692 . Predicting time step:  2014-12-29 19:00:00\n",
      "693 . Predicting time step:  2014-12-29 20:00:00\n",
      "694 . Predicting time step:  2014-12-29 21:00:00\n",
      "695 . Predicting time step:  2014-12-29 22:00:00\n",
      "696 . Predicting time step:  2014-12-29 23:00:00\n",
      "697 . Predicting time step:  2014-12-30 00:00:00\n",
      "698 . Predicting time step:  2014-12-30 01:00:00\n",
      "699 . Predicting time step:  2014-12-30 02:00:00\n",
      "700 . Predicting time step:  2014-12-30 03:00:00\n",
      "701 . Predicting time step:  2014-12-30 04:00:00\n",
      "702 . Predicting time step:  2014-12-30 05:00:00\n",
      "703 . Predicting time step:  2014-12-30 06:00:00\n",
      "704 . Predicting time step:  2014-12-30 07:00:00\n",
      "705 . Predicting time step:  2014-12-30 08:00:00\n",
      "706 . Predicting time step:  2014-12-30 09:00:00\n",
      "707 . Predicting time step:  2014-12-30 10:00:00\n",
      "708 . Predicting time step:  2014-12-30 11:00:00\n",
      "709 . Predicting time step:  2014-12-30 12:00:00\n",
      "710 . Predicting time step:  2014-12-30 13:00:00\n",
      "711 . Predicting time step:  2014-12-30 14:00:00\n",
      "712 . Predicting time step:  2014-12-30 15:00:00\n",
      "713 . Predicting time step:  2014-12-30 16:00:00\n",
      "714 . Predicting time step:  2014-12-30 17:00:00\n",
      "715 . Predicting time step:  2014-12-30 18:00:00\n",
      "716 . Predicting time step:  2014-12-30 19:00:00\n",
      "717 . Predicting time step:  2014-12-30 20:00:00\n",
      "718 . Predicting time step:  2014-12-30 21:00:00\n",
      "719 . Predicting time step:  2014-12-30 22:00:00\n",
      "720 . Predicting time step:  2014-12-30 23:00:00\n",
      "721 . Predicting time step:  2014-12-31 00:00:00\n",
      "CPU times: user 10h 34min 44s, sys: 4h 37min 45s, total: 15h 12min 29s\n",
      "Wall time: 2h 45min 46s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "training_window = 720 # dedicate 30 days (720 hours) for training\n",
    "\n",
    "train_ts = train['load']\n",
    "test_ts = test_shifted\n",
    "\n",
    "history = [x for x in train_ts]\n",
    "history = history[(-training_window):]\n",
    "\n",
    "predictions = list()\n",
    "\n",
    "for t in range(test_ts.shape[0]):\n",
    "    model = SARIMAX(endog=history, order=order, seasonal_order=seasonal_order)\n",
    "    model_fit = model.fit()\n",
    "    yhat = model_fit.forecast(steps = HORIZON)\n",
    "    predictions.append(yhat)\n",
    "    obs = list(test_ts.iloc[t])\n",
    "    # move the training window\n",
    "    history.append(obs[0])\n",
    "    history.pop(0)\n",
    "    print(t+1, '. Predicting time step: ', test_ts.index[t])\n",
    "    # print(t+1, ': predicted =', yhat, 'expected =', obs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare predictions to actual load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>h</th>\n",
       "      <th>prediction</th>\n",
       "      <th>actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-12-01 00:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>2,556.61</td>\n",
       "      <td>2,580.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-12-01 01:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>2,505.36</td>\n",
       "      <td>2,505.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-12-01 02:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>2,445.66</td>\n",
       "      <td>2,482.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-12-01 03:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>2,478.94</td>\n",
       "      <td>2,482.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-12-01 04:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>2,506.77</td>\n",
       "      <td>2,586.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            timestamp    h  prediction   actual\n",
       "0 2014-12-01 00:00:00  t+1    2,556.61 2,580.00\n",
       "1 2014-12-01 01:00:00  t+1    2,505.36 2,505.00\n",
       "2 2014-12-01 02:00:00  t+1    2,445.66 2,482.00\n",
       "3 2014-12-01 03:00:00  t+1    2,478.94 2,482.00\n",
       "4 2014-12-01 04:00:00  t+1    2,506.77 2,586.00"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_df = pd.DataFrame(predictions, columns=['t+'+str(t) for t in range(1, HORIZON+1)])\n",
    "eval_df['timestamp'] = test.index[0:len(test.index)-HORIZON+1]\n",
    "eval_df = pd.melt(eval_df, id_vars='timestamp', value_name='prediction', var_name='h')\n",
    "eval_df['actual'] = np.array(np.transpose(test_ts)).ravel()\n",
    "eval_df[['prediction', 'actual']] = scaler.inverse_transform(eval_df[['prediction', 'actual']])\n",
    "eval_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the mean absolute percentage error over all predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "h\n",
      "t+1    0.01\n",
      "t+10   0.05\n",
      "t+11   0.05\n",
      "t+12   0.05\n",
      "t+13   0.05\n",
      "t+14   0.05\n",
      "t+15   0.05\n",
      "t+16   0.05\n",
      "t+17   0.06\n",
      "t+18   0.06\n",
      "t+19   0.06\n",
      "t+2    0.02\n",
      "t+20   0.06\n",
      "t+21   0.06\n",
      "t+22   0.06\n",
      "t+23   0.07\n",
      "t+24   0.07\n",
      "t+3    0.02\n",
      "t+4    0.03\n",
      "t+5    0.03\n",
      "t+6    0.04\n",
      "t+7    0.04\n",
      "t+8    0.04\n",
      "t+9    0.04\n",
      "Name: APE, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "if(HORIZON > 1):\n",
    "    eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']\n",
    "    print(eval_df.groupby('h')['APE'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One step forecast MAPE:  0.8376112006078202 %\n"
     ]
    }
   ],
   "source": [
    "print('One step forecast MAPE: ', (mape(eval_df[eval_df['h'] == 't+1']['prediction'], eval_df[eval_df['h'] == 't+1']['actual']))*100, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One step forecast MAPE:  4.685256686453014 %\n"
     ]
    }
   ],
   "source": [
    "print('One step forecast MAPE: ', mape(eval_df['prediction'], eval_df['actual'])*100, '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the predictions vs the actuals for the first week of the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RPTA7bsxLI43NIaoW8xjIZID29up+HxA1iuLvBj/p9lsgoNpgoZDKjDaSBaki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tgSKWW+BAjS9KL5KdeCY4cmtIMAcKf63F69Mqa5PSDQeiKb2avTpZxCHwSNbkLY9tp6RPRG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\nwj6XMjQqv+Hp6fF3Tfs34XZOJw8REZFi09MD//Iv8OKLPnFQ2C902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VJ5Hh/yFog65x4C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\niIhIXikQFRERERERkbz6/wF6IlDNwyUs1AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f99763682b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if(HORIZON == 1):\n",
    "    ## Plotting single step forecast\n",
    "    eval_df.plot(x='timestamp', y=['actual', 'prediction'], style=['r', 'b'], figsize=(15, 8))\n",
    "\n",
    "else:\n",
    "    ## Plotting multi step forecast\n",
    "    plot_df = eval_df[(eval_df.h=='t+1')][['timestamp', 'actual']]\n",
    "    for t in range(1, HORIZON+1):\n",
    "        plot_df['t+'+str(t)] = eval_df[(eval_df.h=='t+'+str(t))]['prediction'].values\n",
    "\n",
    "    fig = plt.figure(figsize=(15, 8))\n",
    "    ax = plt.plot(plot_df['timestamp'], plot_df['actual'], color='red', linewidth=4.0)\n",
    "    ax = fig.add_subplot(111)\n",
    "    for t in range(1, HORIZON+1):\n",
    "        x = plot_df['timestamp'][(t-1):]\n",
    "        y = plot_df['t+'+str(t)][0:len(x)]\n",
    "        ax.plot(x, y, color='blue', linewidth=4*math.pow(.9,t), alpha=math.pow(0.8,t))\n",
    "    \n",
    "    ax.legend(loc='best')\n",
    "    \n",
    "plt.xlabel('timestamp', fontsize=12)\n",
    "plt.ylabel('load', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
